1668 Mlflow Jobs - Page 41

Job Description We are seeking a high-impact AI/ML Engineer to lead the design, development, and deployment of machine learning and AI solutions across vision, audio, and language modalities. You'll be part of a fast-paced, outcome-oriented AI & Analytics team, working alongside data scientists, engineers, and product leaders to transform business use cases into real-time, scalable AI systems. This role demands strong technical leadership, a product mindset, and hands-on expertise in Computer Vision, Audio Intelligence, and Deep Learning. Key Responsibilities Architect, develop, and deploy ML models for multimodal problems, including vision (image/video), audio (speech/sound), and NLP tasks. Own the complete ML lifecycle : data ingestion, model development, experimentation, evaluation, deployment, and monitoring. Leverage transfer learning, foundation models, or self-supervised approaches where suitable. Design and implement scalable training pipelines and inference APIs using frameworks like PyTorch or TensorFlow. Collaborate with MLOps, data engineering, and DevOps to productionize models using Docker, Kubernetes, or serverless infrastructure. Continuously monitor model performance and implement retraining workflows to ensure accuracy over time. Stay ahead of the curve on cutting-edge AI research (e.g., generative AI, video understanding, audio embeddings) and incorporate innovations into production systems. Write clean, well-documented, and reusable code to support agile experimentation and long-term platform : Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 58+ years of experience in AI/ML Engineering, with at least 3 years in applied deep learning. Technical Skills Languages : Expert in Python; good knowledge of R or Java is a plus. ML/DL Frameworks : Proficient with PyTorch, TensorFlow, Scikit-learn, ONNX. Computer Vision : Image classification, object detection, OCR, segmentation, tracking (YOLO, Detectron2, OpenCV, MediaPipe). Audio AI : Speech recognition (ASR), sound classification, audio embedding models (Wav2Vec2, Whisper, etc.). Data Engineering : Strong with Pandas, NumPy, SQL, and preprocessing pipelines for structured and unstructured data. NLP/LLMs : Working knowledge of Transformers, BERT/LLAMA, Hugging Face ecosystem is preferred. Cloud & MLOps : Experience with AWS/GCP/Azure, MLFlow, SageMaker, Vertex AI, or Azure ML. Deployment & Infrastructure : Experience with Docker, Kubernetes, REST APIs, serverless ML inference. CI/CD & Version Control : Git, DVC, ML pipelines, Jenkins, Airflow, etc. Soft Skills & Competencies Strong analytical and systems thinking; able to break down business problems into ML components. Excellent communication skills able to explain models, results, and decisions to non-technical stakeholders. Proven ability to work cross-functionally with designers, engineers, product managers, and analysts. Demonstrated bias for action, rapid experimentation, and iterative delivery of impact. (ref:hirist.tech) Show more Show less

Company Description At Blend, we are award-winning experts who transform businesses by delivering valuable insights that make a difference. From crafting a data strategy that focuses resources on what will make the biggest difference to your company, to standing up infrastructure, and turning raw data into value through data science and visualization: we do it all. We believe that data that doesn't drive value is lost opportunity, and we are passionate about helping our clients drive better outcome through applied analytics. We are obsessed with delivering world class solutions to our customers through our network of industry leading partners. If this sounds like your kind of challenge, we would love to hear from you. For more information, visit www.blend360.com Job Description We are looking for someone who is ready for the next step in their career and is excited by the idea of solving problems and designing best in class. However, they also need to be aware of the practicalities of making a difference in the real world – whilst we love innovative advanced solutions, we also believe that sometimes a simple solution can have the most impact. Our AI Engineer is someone who feels the most comfortable around solving problems, answering questions and proposing solutions. We place a high value on the ability to communicate and translate complex analytical thinking into non-technical and commercially oriented concepts, and experience working on difficult projects and/or with demanding stakeholders is always appreciated. What can you expect from the role? Contribute to design, develop, deploy and maintain AI solutions Use a variety of AI Engineering tools and methods to deliver Own parts of projects end-to-end Contributing to solutions design and proposal submissions Supporting the development of the AI engineering team within Blend Maintain in-depth knowledge of the AI ecosystems and trends Mentor junior colleagues Qualifications Contribute to the design, development, testing, deployment, maintenance, and improvement of robust, scalable, and reliable software systems, adhering to best practices. Apply Python programming skills for both software development and AI/ML tasks. Utilize analytical and problem-solving skills to debug complex software, infrastructure, and AI integration issues. Proficiently use version control systems, especially Git and ML/LLMOps model versioning protocols. Assist in analysing complex or ambiguous AI problems, breaking them down into manageable tasks, and contributing to conceptual solution design within the rapidly evolving field of generative AI. Work effectively within a standard software development lifecycle (e.g., Agile, Scrum). Contribute to the design and utilization of scalable systems using cloud services (AWS, Azure, GCP), including compute, storage, and ML/AI services. (Preferred: Azure) Participate in designing and building scalable and reliable infrastructure to support AI inference workloads, including implementing APIs, microservices, and orchestration layers. Contribute to the design, building, or working with event-driven architectures and relevant technologies (e.g., Kafka, RabbitMQ, cloud event services) for asynchronous processing and system integration. Experience with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes, Airflow, Kubeflow, Databricks Jobs, etc). Assist in implementing CI/CD pipelines and optionally using IaC principles/tools for deploying and managing infrastructure and ML/LLM models. Contribute to developing and deploying LLM-powered features into production systems, translating experimental outputs into robust services with clear APIs. Demonstrate familiarity with transformer model architectures and a practical understanding of LLM specifics like context handling. Assist in designing, implementing, and optimising prompt strategies (e.g., chaining, templates, dynamic inputs); practical understanding of output post-processing. Experience integrating with third-party LLM providers, managing API usage, rate limits, token efficiency, and applying best practices for versioning, retries, and failover. Contribute to coordinating multi-step AI workflows, potentially involving multiple models or services, and optimising for latency and cost (sequential vs. parallel execution). Assist in monitoring, evaluating, and optimising AI/LLM solutions for performance (latency, throughput, reliability), accuracy, and cost in production environments. Additional Information Experience specifically with the Databricks MLOps platform. Familiarity with fine-tuning classical LLM models. Experience ensuring security and observability for AI services. Contribution to relevant open-source projects. Familiarity with building agentic GenAI modules or systems. Have hands-on experience implementing and automating MLOps/LLMOps practices, including model tracking, versioning, deployment, monitoring (latency, cost, throughput, reliability), logging, and retraining workflows. Experience working with MLOps/experiment tracking and operational tools (e.g., MLflow, Weights & Biases). Show more Show less

Seeking an experienced AI Architect to lead the development of our AI and Machine Learning infrastructure and specialized language models. This role will establish and lead our MLOps practices and drive the creation of scalable, production-ready AI/ML systems. Key Responsibilities Discuss the feasibility of AI/ML use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings Design and implement robust ML infrastructure and deployment pipelines Establish comprehensive MLOps practices for model training, versioning, and deployment Lead the development of HR-specialized language models (SLMs) Implement model monitoring, observability, and performance optimization frameworks Develop and execute fine-tuning strategies for large language models Create and maintain data quality assessment and validation processes Design model versioning systems and A/B testing frameworks Define technical standards and best practices for AI development Optimize infrastructure for cost, performance, and scalability Required Qualifications 7+ years of experience in ML/AI engineering or related technical roles 3+ years of hands-on experience with MLOps and production ML systems Demonstrated expertise in fine-tuning and adapting foundation models Strong knowledge of model serving infrastructure and orchestration Proficiency with MLOps tools (MLflow, Kubeflow, Weights & Biases, etc.) Experience implementing model versioning and A/B testing frameworks Strong background in data quality methodologies for ML training Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience with cloud-based ML platforms (AWS, Azure, Google Cloud) Proven track record of deploying ML models at scale Preferred Qualifications Experience developing AI applications for enterprise software domains Knowledge of distributed training techniques and infrastructure Experience with retrieval-augmented generation (RAG) systems Familiarity with vector databases (Pinecone, Weaviate, Milvus) Understanding of responsible AI practices and bias mitigation Bachelor's or Master's degree in Computer Science, Machine Learning, or related field

Key Responsibilities Design, develop, and deploy machine learning models for prediction, recommendation, anomaly detection, NLP, or image processing tasks. Work with large, complex datasets to extract insights and build scalable solutions. Collaborate with data engineers to create efficient data pipelines and feature engineering workflows. Evaluate model performance using appropriate metrics and improve models through iterative testing and tuning. Communicate findings, insights, and model outputs clearly to non-technical stakeholders. Stay up to date with the latest machine learning research, frameworks, and technologies. Required Skills Strong programming skills in Python (Pandas, NumPy, Scikit-learn, etc.). Hands-on experience with ML/DL frameworks like TensorFlow, PyTorch, XGBoost, or LightGBM. Experience in building, deploying, and maintaining end-to-end ML models in production. Solid understanding of statistics, probability, and mathematical modeling. Proficiency with SQL and data manipulation in large-scale databases. Familiarity with version control (Git), CI/CD workflows, and model tracking tools (MLflow, DVC, etc.). Preferred Skills Experience with cloud platforms like AWS, GCP, or Azure (e.g., SageMaker, Vertex AI). Knowledge of MLOps practices and tools for scalable ML deployments. Exposure to real-time data processing or streaming (Kafka, Spark). Experience with NLP, Computer Vision, or Time Series Forecasting. Show more Show less

Appnext offers end-to-end discovery solutions covering all the touchpoints users have with their devices. Thanks to Appnext’s direct partnerships with top OEM brands and carriers, user engagement is achieved from the moment they personalize their device for the first time and throughout their daily mobile journey. Appnext ‘Timeline’, a patented behavioral analytics technology, is uniquely capable of predicting the apps users are likely to need next. This innovative solution means app developers and marketers can seamlessly engage with users directly on their smartphones through personalized, contextual recommendations. Established in 2012 and now with 12 offices globally, Appnext is the fastest-growing and largest independent mobile discovery platform in emerging markets. As a Machine Learning Engineer , you will be in charge of building end-to-end machine learning pipelines that operate at a huge scale, from data investigation, ingestions and model training to deployment, monitoring, and continuous optimization. You will ensure that each pipeline delivers measurable impact through experimentation, high-throughput inference, and seamless integration with business-critical systems. This job combines 70% machine learning engineering and 30% algorithm engineering and data science. We're seeking an Adtech pro who thrives in a team environment, possesses exceptional communication and analytical skills, and can navigate high-pressure demands of delivering results, taking ownership, and leveraging sales opportunities. Responsibilities: Build ML pipelines that train on real big data and perform on a massive scale. Handle a massive responsibility, Advertise on lucrative placement (Samsung appstore, Xiaomi phones, TrueCaller). Train models that will make billions of daily predictions and affect hundreds of millions users. Optimize and discover the best solution algorithm to data problems, from implementing exotic losses to efficient grid search. Validate and test everything. Every step should be measured and chosen via AB testing. Use of observability tools. Own your experiments and your pipelines. Be Frugal. Optimize the business solution at minimal cost. Advocate for AI. Be the voice of data science and machine learning, answering business needs. Build future products involving agentic AI and data science. Affect millions of users every instant and handle massive scale Requirements: MSc in CS/EE/STEM with at least 5 years of proven experience (or BSc with equivalent experience) as a Machine Learning Engineer: strong focus on MLOps, data analytics, software engineering, and applied data science- Must Hyper communicator: Ability to work with minimal supervision and maximal transparency. Must understand requirements rigorously, while frequently giving an efficient honest picture of his/hers work progress and results. Flawless verbal English- Must Strong problem-solving skills, drive projects from concept to production, working incrementally and smart. Ability to own features end-to-end, theory, implementation, and measurement. Articulate data-driven communication is also a must. Deep understanding of machine learning, including the internals of all important ML models and ML methodologies. Strong real experience in Python, and at least one other programming language (C#, C++, Java, Go…). Ability to write efficient, clear, and resilient production-grade code. Flawless in SQL. Strong background in probability and statistics. Experience with tools and ML models Experience with conducting A/B test. Experience with using cloud providers and services (AWS) and python frameworks: TensorFlow/PyTorch, Numpy, Pandas, SKLearn (Airflow, MLflow, Transformers, ONNX, Kafka are a plus). AI/LLMs assistance: Candidates have to hold all skills independently without using AI assist. With that candidates are expected to use AI effectively, safely and transparently. Preferred: Deep Knowledge in ML aspects including ML Theory, Optimization, Deep learning tinkering, RL, Uncertainty quantification, NLP, classical machine learning, performance measurement. Prompt engineering and Agentic workflows experience Web development skills Publication in leading machine learning conferences and/or medium blogs. Show more Show less

Responsibilities Build and fine-tune models for NLP, computer vision, predictions, and more. Engineer intelligent pipelines that are used in production. Collaborate across teams to bring AI solutions to life (not just in Jupyter Notebooks). Embrace MLOps with tools like MLflow, Docker, and Kubernetes. Stay on the AI cutting edge and share what you learnmentorship mindset is a big plus. Champion code quality and contribute to a future-focused dev culture. Requirements 3-4 years in hardcore AI/ML or applied data science. Pro-level Python skills (R is cool too, but Python is king here). Mastery over ML frameworks: scikit-learn, XGBoost, LightGBM, TensorFlow/Keras, PyTorch. Hands-on with real-world data wrangling, feature engineering, and model deployment. DevOps-savvy: Docker, REST APIs, Git, and maybe even some MLOps sparkle. Cloud comfort: AWS, GCP, or Azure - take your pick. Solid grasp of Agile, good debugging instincts, and a hunger for optimization. This job was posted by Sampurna Pal from AmpleLogic. Show more Show less

Position: Solution Architect Location: Chennai/ Bangalore/ Kuala Lumpur Experience: 8+ years Employment Type: Full-time Job Overview Join Moving Walls, a trailblazer in the Out-of-Home (OOH) advertising and AdTech ecosystem, as a Solution Architect. This pivotal role places you at the heart of our innovative journey, designing and implementing scalable, efficient, and transformative solutions for our award-winning platforms like LMX and MAX . With a focus on automating and enhancing media transactions, you’ll enable a seamless connection between media buyers and sellers in a rapidly evolving digital-first landscape. As a Solution Architect, you will bridge the gap between business objectives and technical execution, working in an Agile environment with POD-based execution models to ensure ownership and accountability. You will drive initiatives that revolutionize the way data and technology shape OOH advertising. Why Join Us? ● Innovative Vision: Be part of a team committed to "Creating the Future of Outernet Media", where every solution impacts global markets across Asia, ANZ, Africa, and more. ● Cutting-edge Projects: Work on features like programmatic deal automation, data-driven audience insights, and dynamic campaign management for platforms connecting billions of ad impressions. ● Collaborative Culture: Collaborate with multidisciplinary teams, including Sales, Product Management, and Engineering, to craft solutions that are customized and impactful. What You’ll Do: ● Architect scalable and innovative solutions for AdTech products, ensuring alignment with organizational goals and market needs. ● Collaborate with cross-functional teams to gather, analyze, and translate business requirements into technical designs. ● Lead the development of programmatic solutions, dynamic audience segmentation tools, and integrations for global markets. ● Enhance existing products by integrating advanced features like dynamic rate cards, bid management, and inventory mapping. ● Advocate for best practices in system design, ensuring the highest standards of security, reliability, and performance. What You Bring: ● A strong technical background with hands-on experience in cloud-based architectures, API integrations, and data analytics. ● Proven expertise in working within an Agile environment and leading POD-based teams to deliver high-impact results. ● Passion for AdTech innovation and the ability to navigate complex, fast-paced environments. ● Excellent problem-solving skills, creativity, and a customer-centric mindset. Key Responsibilities 1. Solution Design: ○ Develop end-to-end solution architectures for web, mobile, and cloud-based platforms using the specified tech stack. ○ Translate business requirements into scalable and reliable technical solutions. 2. Agile POD-Based Execution: ○ Collaborate with cross-functional POD teams (Product, Engineering, QA, and Operations) to deliver iterative and focused solutions. ○ Ensure clear ownership of deliverables within the POD, fostering accountability and streamlined execution. ○ Contribute to defining and refining the POD stages to ensure alignment with organizational goals. 3. Collaboration and Stakeholder Management: ○ Work closely with product, engineering, and business teams to define technical requirements. ○ Lead technical discussions with internal and external stakeholders. 4. Technical Expertise: ○ Provide architectural guidance and best practices for system integrations, APIs, and microservices. ○ Ensure solutions meet non-functional requirements like scalability, reliability, and security. 5. Documentation: ○ Prepare and maintain architectural documentation, including solution blueprints and workflows. ○ Create technical roadmaps and detailed design documentation. 6. Mentorship: ○ Guide and mentor engineering teams during development and deployment phases. ○ Review code and provide technical insights to improve quality and performance. 7. Innovation and Optimization: ○ Identify areas for technical improvement and drive innovation in solutions. ○ Evaluate emerging technologies to recommend the best tools and frameworks. Required Skills and Qualifications ● Bachelor’s/Master’s degree in Computer Science, Information Technology, or a related field. ● Proven experience as a Solution Architect or a similar role. ● Expertise in programming languages and frameworks: Java, Angular, Python, C++ ● Proficiency in AI/ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, or Keras. ● Experience in deploying AI models in production, including optimizing for performance and scalability. ● Understanding of deep learning, NLP, computer vision, or generative AI techniques. ● Hands-on experience with model fine-tuning, transfer learning, and hyperparameter optimization. ● Strong knowledge of enterprise architecture frameworks (TOGAF, Zachman, etc.). ● Expertise in distributed systems, microservices, and cloud-native architectures. ● Experience in API design, data pipelines, and integration of AI services within existing systems. ● Strong knowledge of databases: MongoDB, SQL, NoSQL. ● Proficiency in working with large-scale datasets, data wrangling, and ETL pipelines. ● Hands-on experience with CI/CD pipelines for AI development. ● Version control systems like Git and experience with ML lifecycle tools such as MLflow or DVC. ● Proven track record of leading AI-driven projects from ideation to deployment. ● Hands-on experience with cloud platforms (AWS, Azure, GCP) for deploying AI solutions. ● Familiarity with Agile methodologies, especially POD-based execution models. ● Strong problem-solving skills and ability to design scalable solutions. ● Excellent communication skills to articulate technical solutions to stakeholders. Preferred Qualifications ● Experience in e-commerce, Adtech or OOH (Out-of-Home) advertising technology. ● Knowledge of tools like Jira, Confluence, and Agile frameworks like Scrum or Kanban. ● Certification in cloud technologies (e.g., AWS Solutions Architect). Tech Stack ● Programming Languages: Java, Python or C++ ● Frontend Framework: Angular ● Database Technologies: MongoDB, SQL, NoSQL ● Cloud Platform: AWS ● Familiarity with data processing tools like Pandas, NumPy, and big data frameworks (e.g., Hadoop, Spark). ● Experience with cloud platforms for AI (AWS SageMaker, Azure ML, Google Vertex AI). ● Understanding of APIs, microservices, and containerization tools like Docker and Kubernetes. Share your profile to kushpu@movingwalls.com Show more Show less

We are looking for an experienced AI Engineer to join our team. The ideal candidate will have a strong background in designing, deploying, and maintaining advanced AI/ML models with expertise in Natural Language Processing (NLP), Computer Vision, and architectures like Transformers and Diffusion Models. You will play a key role in developing AI-powered solutions, optimizing performance, and deploying and managing models in production environments. Key Responsibilities AI Model Development and Optimization: Design, train, and fine-tune AI models for NLP, Computer Vision, and other domains using frameworks like TensorFlow and PyTorch. Work on advanced architectures, including Transformer-based models (e.g., BERT, GPT, T5) for NLP tasks and CNN-based models (e.g., YOLO, VGG, ResNet) for Computer Vision applications. Utilize techniques like PEFT (Parameter-Efficient Fine-Tuning) and SFT (Supervised Fine-Tuning) to optimize models for specific tasks. Build and train RLHF (Reinforcement Learning with Human Feedback) and RL-based models to align AI behavior with real-world objectives., Explore multimodal AI solutions combining text, vision, and audio using generative deep learning architectures. Natural Language Processing (NLP): Develop and deploy NLP solutions, including language models, text generation, sentiment analysis, and text-to-speech systems. Leverage advanced Transformer architectures (e.g., BERT, GPT, T5) for NLP tasks. AI Model Deployment and Frameworks: Deploy AI models using frameworks like VLLM, Docker, and MLFlow in production-grade environments. Create robust data pipelines for training, testing, and inference workflows. Implement CI/CD pipelines for seamless integration and deployment of AI solutions. Production Environment Management: Deploy, monitor, and manage AI models in production, ensuring performance, reliability, and scalability. Set up monitoring systems using Prometheus to track metrics like latency, throughput, and model drift. Data Engineering and Pipelines: Design and implement efficient data pipelines for preprocessing, cleaning, and transformation of large datasets. Integrate with cloud-based data storage and retrieval systems for seamless AI workflows. Performance Monitoring and Optimization: Optimize AI model performance through hyperparameter tuning and algorithmic improvements. Monitor performance using tools like Prometheus, tracking key metrics (e.g., latency, accuracy, model drift, error rates etc.) Solution Design and Architecture: Collaborate with cross-functional teams to understand business requirements and translate them into scalable, efficient AI/ML solutions. Design end-to-end AI systems, including data pipelines, model training workflows, and deployment architectures, ensuring alignment with business objectives and technical constraints. Conduct feasibility studies and proof-of-concepts (PoCs) for emerging technologies to evaluate their applicability to specific use cases. Stakeholder Engagement: Act as the technical point of contact for AI/ML projects, managing expectations and aligning deliverables with timelines. Participate in workshops, demos, and client discussions to showcase AI capabilities and align solutions with client needs. Experience: 2 - 6 years of experience Salary : 5-11 LPA Job Types: Full-time, Permanent Pay: ₹500,000.00 - ₹1,100,000.00 per year Schedule: Day shift Work Location: In person

Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. 3+ years of experience in AI/ML development and deployment. Proficient in Python and familiar with libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy. Strong understanding of machine learning algorithms (supervised, unsupervised, deep learning). Experience with cloud platforms (AWS, Azure, GCP) and MLOps tools (MLflow, Airflow, Docker, Kubernetes). Solid understanding of data structures, algorithms, and software engineering principles. Experience with RESTful APIs and integrating AI models into production environments. Job Type: Full-time Pay: ₹35,000.00 - ₹60,000.00 per month Benefits: Internet reimbursement Paid sick time Provident Fund Schedule: Fixed shift Work Location: In person

AI Specialist / Machine Learning Engineer Location: On-site (hyderabad) Department: Data Science & AI Innovation Experience Level: Mid–Senior Reports To: Director of AI / CTO Employment Type: Full-time Job Summary We are seeking a skilled and forward-thinking AI Specialist to join our advanced technology team. In this role, you will lead the design, development, and deployment of cutting-edge AI/ML solutions, including large language models (LLMs), multimodal systems, and generative AI. You will collaborate with cross-functional teams to develop intelligent systems, automate complex workflows, and unlock insights from data at scale. Key Responsibilities Design and implement machine learning models for natural language processing (NLP), computer vision, predictive analytics, and generative AI. Fine-tune and deploy LLMs using frameworks such as Hugging Face Transformers, OpenAI APIs, and Anthropic Claude. Develop Retrieval-Augmented Generation (RAG) pipelines using tools like LangChain, LlamaIndex, and vector databases (e.g., Pinecone, Weaviate, Qdrant). Productionize ML workflows using MLflow, TensorFlow Extended (TFX), or AWS SageMaker Pipelines. Integrate generative AI with business applications, including Copilot-style features, chat interfaces, and workflow automation. Collaborate with data scientists, software engineers, and product managers to build and scale AI-powered products. Monitor, evaluate, and optimize model performance, focusing on fairness, explainability (e.g., SHAP, LIME), and data/model drift. Stay informed on cutting-edge AI research (e.g., NeurIPS, ICLR, arXiv) and evaluate its applicability to business challenges. Tools & Technologies Languages & Frameworks Python, PyTorch, TensorFlow, JAX FastAPI, LangChain, LlamaIndex ML & AI Platforms OpenAI (GPT-4/4o), Anthropic Claude, Mistral, Cohere Hugging Face Hub & Transformers Google Vertex AI, AWS SageMaker, Azure ML Data & Deployment MLflow, DVC, Apache Airflow, Ray Docker, Kubernetes, RESTful APIs, GraphQL Snowflake, BigQuery, Delta Lake Vector Databases & RAG Tools Pinecone, Weaviate, Qdrant, FAISS ChromaDB, Milvus Generative & Multimodal AI DALL·E, Sora, Midjourney, Runway Whisper, CLIP, SAM (Segment Anything Model) Qualifications Bachelor’s or Master’s in Computer Science, AI, Data Science, or related discipline 3–7 years of experience in machine learning or applied AI Hands-on experience deploying ML models to production environments Familiarity with LLM prompt engineering and fine-tuning Strong analytical thinking, problem-solving ability, and communication skills Preferred Qualifications Contributions to open-source AI projects or academic publications Experience with multi-agent frameworks (e.g., AutoGPT, OpenDevin) Knowledge of synthetic data generation and augmentation techniques Job Type: Permanent Pay: ₹734,802.74 - ₹1,663,085.14 per year Benefits: Health insurance Provident Fund Schedule: Day shift Work Location: In person

This posting is for one of our International Clients. About the Role We’re creating a new certification: Inside Gemini: Gen AI Multimodal and Google Intelligence (Google DeepMind) . This course is designed for technical learners who want to understand and apply the capabilities of Google’s Gemini models and DeepMind technologies to build powerful, multimodal AI applications. We’re looking for a Subject Matter Expert (SME) who can help shape this course from the ground up. You’ll work closely with a team of learning experience designers, writers, and other collaborators to ensure the course is technically accurate, industry-relevant, and instructionally sound. Responsibilities As the SME, you’ll partner with learning experience designers and content developers to: Translate real-world Gemini and DeepMind applications into accessible, hands-on learning for technical professionals. Guide the creation of labs and projects that allow learners to build pipelines for image-text fusion, deploy Gemini APIs, and experiment with DeepMind’s reinforcement learning libraries. Contribute technical depth across activities, from high-level course structure down to example code, diagrams, voiceover scripts, and data pipelines. Ensure all content reflects current, accurate usage of Google’s multimodal tools and services. Be available during U.S. business hours to support project milestones, reviews, and content feedback. This role is an excellent fit for professionals with deep experience in AI/ML, Google Cloud, and a strong familiarity with multimodal systems and the DeepMind ecosystem. Essential Tools & Platforms A successful SME in this role will demonstrate fluency and hands-on experience with the following: Google Cloud Platform (GCP) Vertex AI (particularly Gemini integration, model tuning, and multimodal deployment) Cloud Functions, Cloud Run (for inference endpoints) BigQuery and Cloud Storage (for handling large image-text datasets) AI Platform Notebooks or Colab Pro Google DeepMind Technologies JAX and Haiku (for neural network modeling and research-grade experimentation) DeepMind Control Suite or DeepMind Lab (for reinforcement learning demonstrations) RLax or TF-Agents (for building and modifying RL pipelines) AI/ML & Multimodal Tooling Gemini APIs and SDKs (image-text fusion, prompt engineering, output formatting) TensorFlow 2.x and PyTorch (for model interoperability) Label Studio, Cloud Vision API (for annotation and image-text preprocessing) Data Science & MLOps DVC or MLflow (for dataset and model versioning) Apache Beam or Dataflow (for processing multimodal input streams) TensorBoard or Weights & Biases (for visualization) Content Authoring & Collaboration GitHub or Cloud Source Repositories Google Docs, Sheets, Slides Screen recording tools like Loom or OBS Studio Required skills and experience: Demonstrated hands-on experience building, deploying, and maintaining sophisticated AI powered applications using Gemini APIs/SDKs within the Google Cloud ecosystem, especially in Firebase Studio and VS Code. Proficiency in designing and implementing agent-like application patterns, including multi-turn conversational flows, state management, and complex prompting strategies (e.g., Chain-of Thought, few-shot, zero-shot). Experience integrating Gemini with Google Cloud services (Firestore, Cloud Functions, App Hosting) and external APIs for robust, production-ready solutions. Proven ability to engineer applications that process, integrate, and generate content across multiple modalities (text, images, audio, video, code) using Gemini’s native multimodal capabilities. Skilled in building and orchestrating pipelines for multimodal data handling, synchronization, and complex interaction patterns within application logic. Experience designing and implementing production-grade RAG systems, including integration with vector databases (e.g., Pinecone, ChromaDB) and engineering data pipelines for indexing and retrieval. Ability to manage agent state, memory, and persistence for multi-turn and long-running interactions. Proficiency leveraging AI-assisted coding features in Firebase Studio (chat, inline code, command execution) and using App Prototyping agents or frameworks like Genkit for rapid prototyping and structuring agentic logic. Strong command of modern development workflows, including Git/GitHub, code reviews, and collaborative development practices. Experience designing scalable, fault-tolerant deployment architectures for multimodal and agentic AI applications using Firebase App Hosting, Cloud Run, or similar serverless/cloud platforms. Advanced MLOps skills, including monitoring, logging, alerting, and versioning for generative AI systems and agents. Deep understanding of security best practices: prompt injection mitigation (across modalities), secure API key management, authentication/authorization, and data privacy. Demonstrated ability to engineer for responsible AI, including bias detection, fairness, transparency, and implementation of safety mechanisms in agentic and multimodal applications. Experience addressing ethical challenges in the deployment and operation of advanced AI systems. Proven success designing, reviewing, and delivering advanced, project-based curriculum and hands-on labs for experienced software developers and engineers. Ability to translate complex engineering concepts (RAG, multimodal integration, agentic patterns, MLOps, security, responsible AI) into clear, actionable learning materials and real world projects. 5+ years of professional experience in AI-powered application development, with a focus on generative and multimodal AI. Strong programming skills in Python and JavaScript/TypeScript; experience with modern frameworks and cloud-native development. Bachelor’s or Master’s degree in Computer Science, Data Engineering, AI, or a related technical field. Ability to explain advanced technical concepts (e.g., fusion transformers, multimodal embeddings, RAG workflows) to learners in an accessible way. Strong programming experience in Python and experience deploying machine learning pipelines Ability to work independently, take ownership of deliverables, and collaborate closely with designers and project managers Preferred: Experience with Google DeepMind tools (JAX, Haiku, RLax, DeepMind Control Suite/Lab) and reinforcement learning pipelines. Familiarity with open data formats (Delta, Parquet, Iceberg) and scalable data engineering practices. Prior contributions to open-source AI projects or technical community engagement. Show more Show less

Strong knowledge of Python, R, and ML frameworks such as scikit-learn, TensorFlow, PyTorch. Experience with cloud ML platforms: SageMaker, Azure ML, Vertex AI.LLM Experience such as GPT Hands-on experience with data wrangling, feature engineering, and model optimization. Also experienced in developing model wrapers. Deep understanding of algorithms including regression, classification, clustering, NLP, and deep learning. Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow.

Strong understating of Python, ML concepts and frameworks, Fast API, Graph QL Experience in developing scalable APIs. Knowledge of AWS, preferred services are storage, EC2, Kubernetes Exposure of ML best practices, documentation and unit testing. ML Flow, AirFlow, ML pipeline creation, drift monitoring and control Experience in developing and deploying machine learning models in a production environment using CI/CD. Communicate with clients to understand requirements and ask right questions. Knowledge of Django and database design will be added advantage. Strong analytical and problem-solving skills. Standards : Model Deployment Standards Use standardized APIs (e.g., RESTful) to interface with models Implement model versioning and proper naming conventions Monitoring and Maintenance Schedule routine model retraining and monitoring Code Quality Standards Follow style guides (e.g., PEP 8 in Python) Write comprehensive debugging and tests

Company: Indian / Global Engineering & Manufacturing Organization Key Skills: Machine Learning, ML, AI Artificial intelligence, Artificial Intelligence, Tensorflow, Python, Pytorch. Roles and Responsibilities: Design, build, and rigorously optimize the complete stack necessary for large-scale model training, fine-tuning, and inference--including dataloading, distributed training, and model deployment--to maximize Model Flop Utilization (MFU) on compute clusters. Collaborate closely with research scientists to translate state-of-the-art models and algorithms into production-grade, high-performance code and scalable infrastructure. Implement, integrate, and test advancements from recent research publications and open-source contributions into enterprise-grade systems. Profile training workflows to identify and resolve bottlenecks across all layers of the training stack--from input pipelines to inference--enhancing speed and resource efficiency. Contribute to evaluations and selections of hardware, software, and cloud platforms defining the future of the AI infrastructure stack. Use MLOps tools (e.g., MLflow, Weights & Biases) to establish best practices across the entire AI model lifecycle, including development, validation, deployment, and monitoring. Maintain extensive documentation of infrastructure architecture, pipelines, and training processes to ensure reproducibility and smooth knowledge transfer. Continuously research and implement improvements in large-scale training strategies and data engineering workflows to keep the organization at the cutting edge. Demonstrate initiative and ownership in developing rapid prototypes and production-scale systems for AI applications in the energy sector. Experience Requirement: 5-9 years of experience building and optimizing large-scale machine learning infrastructure, including distributed training and data pipelines. Proven hands-on expertise with deep learning frameworks such as PyTorch, JAX, or PyTorch Lightning in multi-node GPU environments. Experience in scaling models trained on large datasets across distributed computing systems. Familiarity with writing and optimizing CUDA, Triton, or CUTLASS kernels for performance enhancement is preferred. Hands-on experience with AI/ML lifecycle management using MLOps frameworks and performance profiling tools. Demonstrated collaboration with AI researchers and data scientists to integrate models into production environments. Track record of open-source contributions in AI infrastructure or data engineering is a significant plus. Education: M.E., B.Tech M.Tech (Dual), BCA, B.E., B.Tech, M. Tech, MCA. Show more Show less

ZS s Beyond Healthcare Analytics (BHCA) Team is shaping one of the key growth vector area for ZS, Beyond Healthcare engagement, comprising of clients from industries like Quick service restaurants, Technology, Food & Beverage, Hospitality, Travel, Insurance, Consumer Products Goods & other such industries across North America, Europe & South East Asia region. BHCA India team currently has presence across New Delhi, Pune and Bengaluru offices and is continuously expanding further at a great pace. BHCA India team works with colleagues across clients and geographies to create and deliver real world pragmatic solutions leveraging AI SaaS products & platforms, Generative AI applications, and other Advanced analytics solutions at scale. What You ll Do: Build, Refine and Use ML Engineering platforms and components. Scaling machine learning algorithms to work on massive data sets and strict SLAs. Build and orchestrate model pipelines including feature engineering, inferencing and continuous model training. Implement ML Ops including model KPI measurements, tracking, model drift & model feedback loop. Collaborate with client facing teams to understand business context at a high level and contribute in technical requirement gathering. Implement basic features aligning with technical requirements. Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors. Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews. Write unit tests as well as higher level tests to handle expected edge cases and errors gracefully, as well as happy paths. Uses bug tracking, code review, version control and other tools to organize and deliver work. Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues and dependencies. Consistently contribute in researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions. What You ll Bring A master's or bachelor s degree in Computer Science or related field from a top university. 4+ years hands-on experience in ML development. Good understanding of the fundamentals of machine learning Strong programming expertise in Python, PySpark/Scala. Expertise in crafting ML Models for high performance and scalability. Experience in implementing feature engineering, inferencing pipelines, and real time model predictions. Experience in ML Ops to measure and track model performance, experience working with MLFlow Experience with Spark or other distributed computing frameworks. Experience in ML platforms like Sage maker, Kubeflow. Experience with pipeline orchestration tools such Airflow. Experience in deploying models to cloud services like AWS, Azure, GCP, Azure ML. Expertise in SQL, SQL DB's. Knowledgeable of core CS concepts such as common data structures and algorithms. Collaborate well with teams with different backgrounds / expertise / functions

You will lead the development of predictive machine learning models for Revenue Cycle Management analytics, along the lines of: 1 Payer Propensity Modeling - predicting payer behavior and reimbursement likelihood 2 Claim Denials Prediction - identifying high-risk claims before submission 3 Payment Amount Prediction - forecasting expected reimbursement amounts 4 Cash Flow Forecasting - predicting revenue timing and patterns 5 Patient-Related Models - enhancing patient financial experience and outcomes 6 Claim Processing Time Prediction - optimizing workflow and resource allocation Additionally, we will work on emerging areas and integration opportunities—for example, denial prediction + appeal success probability or prior authorization prediction + approval likelihood models. You will reimagine how providers, patients, and payors interact within the healthcare ecosystem through intelligent automation and predictive insights, ensuring that providers can focus on delivering the highest quality patient care. VHT Technical Environment 1 Cloud Platform: AWS (SageMaker, S3, Redshift, EC2) 2 Development Tools: Jupyter Notebooks, Git, Docker 3 Programming: Python, SQL, R (optional) 4 ML/AI Stack: Scikit-learn, TensorFlow/PyTorch, MLflow, Airflow 5 Data Processing: Spark, Pandas, NumPy 6 Visualization: Matplotlib, Seaborn, Plotly, Tableau Show more Show less

We are seeking a highly skilled Senior Technical Architect with expertise in Databricks, Apache Spark, and modern data engineering architectures. The ideal candidate will have a strong grasp of Generative AI and RAG pipelines and a keen interest (or working knowledge) in Agentic AI systems. This individual will lead the architecture, design, and implementation of scalable data platforms and AI-powered applications for our global clients. This high-impact role requires technical leadership, cross-functional collaboration, and a passion for solving complex business challenges with data and AI. Responsibilities Lead architecture, design, and deployment of scalable data solutions using Databricks and the medallion architecture. Guide technical teams in building batch and streaming data pipelines using Spark, Delta Lake, and MLflow. Collaborate with clients and internal stakeholders to understand business needs and translate them into robust data and AI architectures. Design and prototype Generative AI applications using LLMs, RAG pipelines, and vector stores. Provide thought leadership on the adoption of Agentic AI systems in enterprise environments. Mentor data engineers and solution architects across multiple projects. Ensure adherence to security, governance, performance, and reliability best practices. Stay current with emerging trends in data engineering, MLOps, GenAI, and agent-based systems. Qualifications Bachelor's or Master's degree in Computer Science, Engineering, or related technical discipline. 10+ years of experience in data architecture, data engineering, or software architecture roles. 5+ years of hands-on experience with Databricks, including Spark SQL, Delta Lake, Unity Catalog, and MLflow. Proven experience in designing and delivering production-grade data platforms and pipelines. Exposure to LLM frameworks (OpenAI, Hugging Face, LangChain, etc.) and vector databases (FAISS, Weaviate, etc.). Strong understanding of cloud platforms (Azure, AWS, or GCP), particularly in the context of Databricks deployment. Knowledge or interest in Agentic AI frameworks and multi-agent system design is highly desirable. Technical Skills Databricks (incl. Spark, Delta Lake, MLflow, Unity Catalog) Python, SQL, PySpark GenAI tools and libraries (LangChain, OpenAI, etc.) CI/CD and DevOps for data REST APIs, JSON, data serialization formats Cloud services (Azure/AWS/GCP) Soft Skills Strong communication and stakeholder management skills Ability to lead and mentor diverse technical teams Strategic thinking with a bias for action Comfortable with ambiguity and iterative development Client-first mindset and consultative approach Excellent problem-solving and analytical skills Preferred Certifications Databricks Certified Data Engineer / Architect Cloud certifications (Azure/AWS/GCP) Any certifications in AI/ML, NLP, or GenAI frameworks are a plus Show more Show less

Job Description We are seeking a highly skilled Senior Technical Architect with expertise in Databricks, Apache Spark, and modern data engineering architectures. The ideal candidate will have a strong grasp of Generative AI and RAG pipelines and a keen interest (or working knowledge) in Agentic AI systems. This individual will lead the architecture, design, and implementation of scalable data platforms and AI-powered applications for our global clients. This high-impact role requires technical leadership, cross-functional collaboration, and a passion for solving complex business challenges with data and AI. Key Responsibilities Lead architecture, design, and deployment of scalable data solutions using Databricks and the medallion architecture. Guide technical teams in building batch and streaming data pipelines using Spark, Delta Lake, and MLflow. Collaborate with clients and internal stakeholders to understand business needs and translate them into robust data and AI architectures. Design and prototype Generative AI applications using LLMs, RAG pipelines, and vector stores. Provide thought leadership on the adoption of Agentic AI systems in enterprise environments. Mentor data engineers and solution architects across multiple projects. Ensure adherence to security, governance, performance, and reliability best practices. Stay current with emerging trends in data engineering, MLOps, GenAI, and agent-based systems. Qualifications Bachelor's or Master's degree in Computer Science, Engineering, or related technical discipline. 10+ years of experience in data architecture, data engineering, or software architecture roles. 5+ years of hands-on experience with Databricks, including Spark SQL, Delta Lake, Unity Catalog, and MLflow. Proven experience in designing and delivering production-grade data platforms and pipelines. Exposure to LLM frameworks (OpenAI, Hugging Face, LangChain, etc.) and vector databases (FAISS, Weaviate, etc.). Strong understanding of cloud platforms (Azure, AWS, or GCP), particularly in the context of Databricks deployment. Knowledge or interest in Agentic AI frameworks and multi-agent system design is highly desirable. Technical Skills Databricks (incl. Spark, Delta Lake, MLflow, Unity Catalog) Python, SQL, PySpark GenAI tools and libraries (LangChain, OpenAI, etc.) CI/CD and DevOps for data REST APIs, JSON, data serialization formats Cloud services (Azure/AWS/GCP) Soft Skills Strong communication and stakeholder management skills Ability to lead and mentor diverse technical teams Strategic thinking with a bias for action Comfortable with ambiguity and iterative development Client-first mindset and consultative approach Excellent problem-solving and analytical skills Preferred Certifications Databricks Certified Data Engineer / Architect Cloud certifications (Azure/AWS/GCP) Any certifications in AI/ML, NLP, or GenAI frameworks are a plus Show more Show less

Technical Expertise : (minimum 2 year relevant experience) ● Solid understanding of Generative AI models and Natural Language Processing (NLP) techniques, including Retrieval-Augmented Generation (RAG) systems, text generation, and embedding models. ● Exposure to Agentic AI concepts, multi-agent systems, and agent development using open-source frameworks like LangGraph and LangChain. ● Hands-on experience with modality-specific encoder models (text, image, audio) for multi-modal AI applications. ● Proficient in model fine-tuning, prompt engineering, using both open-source and proprietary LLMs. ● Experience with model quantization, optimization, and conversion techniques (FP32 to INT8, ONNX, TorchScript) for efficient deployment, including edge devices. ● Deep understanding of inference pipelines, batch processing, and real-time AI deployment on both CPU and GPU. ● Strong MLOps knowledge with experience in version control, reproducible pipelines, continuous training, and model monitoring using tools like MLflow, DVC, and Kubeflow. ● Practical experience with scikit-learn, TensorFlow, and PyTorch for experimentation and production-ready AI solutions. ● Familiarity with data preprocessing, standardization, and knowledge graphs (nice to have). ● Strong analytical mindset with a passion for building robust, scalable AI solutions. ● Skilled in Python, writing clean, modular, and efficient code. ● Proficient in RESTful API development using Flask, FastAPI, etc., with integrated AI/ML inference logic. ● Experience with MySQL, MongoDB, and vector databases like FAISS, Pinecone, or Weaviate for semantic search. ● Exposure to Neo4j and graph databases for relationship-driven insights. ● Hands-on with Docker and containerization to build scalable, reproducible, and portable AI services. ● Up-to-date with the latest in GenAI, LLMs, Agentic AI, and deployment strategies. ● Strong communication and collaboration skills, able to contribute in cross-functional and fast-paced environments. Bonus Skills ● Experience with cloud deployments on AWS, GCP, or Azure, including model deployment and model inferencing. ● Working knowledge of Computer Vision and real-time analytics using OpenCV, YOLO, and similar Show more Show less

Orion Innovation is a premier, award-winning, global business and technology services firm. Orion delivers game-changing business transformation and product development rooted in digital strategy, experience design, and engineering, with a unique combination of agility, scale, and maturity. We work with a wide range of clients across many industries including financial services, professional services, telecommunications and media, consumer products, automotive, industrial automation, professional sports and entertainment, life sciences, ecommerce, and education. Summary We are looking for a highly experienced Senior Technical Architect – Gen AI/ML who will play a key role in shaping and driving the architecture and technology strategy for Generative AI and Machine Learning based applications. This is a hands-on leadership role requiring deep technical expertise in AI/ML, strong knowledge of cloud platforms (preferably Microsoft Azure), and the ability to design scalable, secure, and high-performance systems. The ideal candidate is a solution visionary and technical leader who can partner with cross-functional teams to define, design, and deliver cutting-edge AI-driven solutions. Key Responsibilities Solution Architecture & Design Architect end-to-end AI/ML and Gen AI solutions tailored to business needs across multiple domains. Define system architecture, technology stack, and integration strategy for AI-enabled products and platforms. Establish architecture best practices, reusable components, and reference implementations. Hands-on Implementation Lead from the front with prototyping, POCs, and reference models using tools like Azure OpenAI, Hugging Face, LangChain, MLflow, PyTorch, TensorFlow, etc. Drive integration of AI/LLM solutions (RAG, multi-agent systems, embeddings, etc.) into enterprise applications. Evaluate and optimize model performance using advanced metrics and testing. Ensure performance tuning, security, scalability, and reliability of AI solutions. Cloud & MLOps Leverage Azure cloud services such as Azure Machine Learning, Azure OpenAI, Cognitive Services, Azure Kubernetes Service, etc. Design and implement CI/CD pipelines, model versioning, deployment, monitoring, and governance frameworks using MLOps best practices. Technical Skills Strong expertise in Generative AI, LLMs, and ML/DL models. GenAI & LLMs: RAG, LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, Autogen, TaskWeave RAG Evaluation with experience in RAGA frameworks, DeepEval Hands-on with Azure AI stack – Azure OpenAI, Azure ML, Cognitive Services, Synapse, AKS, with familiarity in AWS/GCP as a plus Proficient in Python, C#, Microservices, PyTorch, TensorFlow, LangChain, Vector DBs (Pinecone, LanceDB, FAISS, etc.) Solid understanding of model fine-tuning, prompt engineering, RLHF, and embedding techniques. Deep experience in designing and deploying ML/AI pipelines using Azure, MLOps, Docker, Kubernetes, and API Gateways. Goos understanding of LLM-powered agents, toolchains (e.g., LangChain Agents, Semantic Kernel), and how to embed reasoning and memory into AI-driven solutions. Experience designing and implementing MCP-based orchestration layers to modularize and scale interactions with LLMs and Gen AI components. Experience 10+ years in software architecture and enterprise application development. 3+ years of experience leading Gen AI / ML solutions in production environments. Experience with enterprise-grade solutioning, technical due diligence, and stakeholder management. Proven record of working on Brownfield project. Orion is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, citizenship status, disability status, genetic information, protected veteran status, or any other characteristic protected by law. Candidate Privacy Policy Orion Systems Integrators, LLC And Its Subsidiaries And Its Affiliates (collectively, “Orion,” “we” Or “us”) Are Committed To Protecting Your Privacy. This Candidate Privacy Policy (orioninc.com) (“Notice”) Explains What information we collect during our application and recruitment process and why we collect it; How we handle that information; and How to access and update that information. Your use of Orion services is governed by any applicable terms in this notice and our general Privacy Policy. Show more Show less

neoBIM is a well-funded start-up software company revolutionizing the way architects design buildings with our innovative BIM (Building Information Modelling) software. As we continue to grow, we are building a small and talented team of developers to drive our software forward. Tasks We are looking for a highly skilled Generative AI Developer to join our AI team. The ideal candidate should have strong expertise in deep learning, large language models (LLMs), multimodal AI, and generative models (GANs, VAEs, Diffusion Models, or similar techniques) . This role offers the opportunity to work on cutting-edge AI solutions, from training models to deploying AI-driven applications that redefine automation and intelligence. Develop, fine-tune, and optimize Generative AI models , including LLMs, GANs, VAEs, Diffusion Models, and Transformer-based architectures . Work with large-scale datasets and design self-supervised or semi-supervised learning pipelines . Implement multimodal AI systems that combine text, images, audio, and structured data. Optimize AI model inference for real-time applications and large-scale deployment. Build AI-driven applications for BIM (Building Information Modeling), content generation, and automation . Collaborate with data scientists, software engineers, and domain experts to integrate AI into production. Stay ahead of AI research trends and incorporate state-of-the-art methodologies . Deploy models using cloud-based ML pipelines (AWS/GCP/Azure) and edge computing solutions . Requirements Must-Have Skills Strong programming skills in Python (PyTorch, TensorFlow, JAX, or equivalent). Experience in training and fine-tuning Large Language Models (LLMs) like GPT, BERT, LLaMA, or Mixtral . Expertise in Generative AI techniques , including Diffusion Models (e.g., Stable Diffusion, DALL-E, Imagen), GANs, VAEs . Hands-on experience with transformer-based architectures (e.g., Vision Transformers, BERT, T5, GPT, etc.) . Experience with MLOps frameworks for scaling AI applications (Docker, Kubernetes, MLflow, etc.). Proficiency in data preprocessing, feature engineering, and AI pipeline development . Strong background in mathematics, statistics, and optimization related to deep learning. Good-to-Have Skills Experience in NeRFs (Neural Radiance Fields) for 3D generative AI . Knowledge of AI for Architecture, Engineering, and Construction (AEC) . Understanding of distributed computing (Ray, Spark, or Tensor Processing Units). Familiarity with AI model compression and inference optimization (ONNX, TensorRT, quantization techniques) . Experience in cloud-based AI development (AWS/GCP/Azure) . Benefits Work on high-impact AI projects at the cutting edge of Generative AI . Competitive salary with growth opportunities. Access to high-end computing resources for AI training & development. A collaborative, research-driven culture focused on innovation & real-world impact . Flexible work environment with remote options. Show more Show less

Job Title: Senior Machine Learning Engineer Location: On Site / [Gurgaon, India] Experience: 5+ Years Type: Full-time / Contract About the Role We are looking for an experienced Machine Learning Engineer with a strong background in building, deploying, and scaling ML models in production environments. You will work closely with Data Scientists, Engineers, and Product teams to translate business challenges into data-driven solutions and build robust, scalable ML pipelines. This is a hands-on role requiring a blend of applied machine learning, data engineering, and software development skills. Key Responsibilities Design, build, and deploy machine learning models to solve real-world business problems Work on the end-to-end ML lifecycle: data preprocessing, feature engineering, model selection, training, evaluation, deployment, and monitoring Collaborate with cross-functional teams to identify opportunities for machine learning across products and workflows Develop and optimize scalable data pipelines to support model development and inference Implement model retraining, versioning, and performance tracking in production Ensure models are interpretable, explainable, and aligned with fairness, ethics, and compliance standards Continuously evaluate new ML techniques and tools to improve accuracy and efficiency Document processes, experiments, and findings for reproducibility and team knowledge-sharing Requirements 5+ years of hands-on experience in machine learning, applied data science, or related roles Strong foundation in ML algorithms (regression, classification, clustering, NLP, time series, etc.) Experience with production-level ML deployment using tools like MLflow, Kubeflow, Airflow, FastAPI , or similar Proficiency in Python and libraries like scikit-learn, TensorFlow, PyTorch, XGBoost, pandas, NumPy Experience with cloud platforms (AWS, GCP, or Azure) and containerized environments (Docker, Kubernetes) Strong understanding of software engineering principles and experience with Git, CI/CD, and version control Experience with large datasets, distributed systems (Spark/Databricks), and SQL/NoSQL databases Excellent problem-solving, communication, and collaboration skills Nice to Have Experience with LLMs, Generative AI , or transformer-based models Familiarity with MLOps best practices and infrastructure as code (e.g., Terraform) Experience working in regulated industries (e.g., finance, healthcare) Contributions to open-source projects or ML research papers Why Join Us Work on impactful problems with cutting-edge ML technologies Collaborate with a diverse, expert team across engineering, data, and product Flexible working hours and remote-first culture Opportunities for continuous learning, mentorship, and growth Show more Show less

Organization Snapshot: Birdeye is the leading all-in-one Experience Marketing platform , trusted by over 100,000+ businesses worldwide to power customer acquisition, engagement, and retention through AI-driven automation and reputation intelligence. From local businesses to global enterprises, Birdeye enables brands to deliver exceptional customer experiences across every digital touchpoint. As we enter our next phase of global scale and product-led growth , AI is no longer an add-on—it’s at the very heart of our innovation strategy . Our future is being built on Large Language Models (LLMs), Generative AI, Conversational AI, and intelligent automation that can personalize and enhance every customer interaction in real time. Job Overview: Birdeye is seeking a Senior Data Scientist – NLP & Generative AI to help reimagine how businesses interact with customers at scale through production-grade, LLM-powered AI systems . If you’re passionate about building autonomous, intelligent, and conversational systems , this role offers the perfect platform to shape the next generation of agentic AI technologies. As part of our core AI/ML team, you'll design, deploy, and optimize end-to-end intelligent systems —spanning LLM fine-tuning , Conversational AI , Natural Language Understanding (NLU) , Retrieval-Augmented Generation (RAG) , and Autonomous Agent frameworks . This is a high-impact IC role ideal for technologists who thrive at the intersection of deep NLP research and scalable engineering . Key Responsibilities: LLM, GenAI & Agentic AI Systems Architect and deploy LLM-based frameworks using GPT, LLaMA, Claude, Mistral, and open-source models. Implement fine-tuning , LoRA , PEFT , instruction tuning , and prompt tuning strategies for production-grade performance. Build autonomous AI agents with tool use , short/long-term memory , planning , and multi-agent orchestration (using LangChain Agents, Semantic Kernel, Haystack, or custom frameworks). Design RAG pipelines with vector databases ( Pinecone , FAISS , Weaviate ) for domain-specific contextualization. Conversational AI & NLP Engineering Build Transformer-based Conversational AI systems for dynamic, goal-oriented dialog—leveraging orchestration tools like LangChain, Rasa, and LLMFlow. Implement NLP solutions for semantic search , NER , summarization , intent detection , text classification , and knowledge extraction . Integrate modern NLP toolkits: SpaCy, BERT/RoBERTa, GloVe, Word2Vec, NLTK , and HuggingFace Transformers . Handle multilingual NLP, contextual embeddings, and dialogue state tracking for real-time systems. Scalable AI/ML Engineering Build and serve models using Python , FastAPI , gRPC , and REST APIs . Containerize applications with Docker , deploy using Kubernetes , and orchestrate with CI/CD workflows. Ensure production-grade reliability, latency optimization, observability, and failover mechanisms. Cloud & MLOps Infrastructure Deploy on AWS SageMaker , Azure ML Studio , or Google Vertex AI , integrating with serverless and auto-scaling services. Own end-to-end MLOps pipelines : model training, versioning, monitoring, and retraining using MLflow , Kubeflow , or TFX . Cross-Functional Collaboration Partner with Product, Engineering, and Design teams to define AI-first experiences. Translate ambiguous business problems into structured ML/AI projects with measurable ROI. Contribute to roadmap planning, POCs, technical whitepapers, and architectural reviews. Technical Skillset Required Programming : Expert in Python , with strong OOP and data structure fundamentals. Frameworks : Proficient in PyTorch , TensorFlow , Hugging Face Transformers , LangChain , OpenAI/Anthropic APIs . NLP/LLM : Strong grasp of Transformer architecture , Attention mechanisms , self-supervised learning , and LLM evaluation techniques . MLOps : Skilled in CI/CD tools, FastAPI , Docker , Kubernetes , and deployment automation on AWS/Azure/GCP . Databases : Hands-on with SQL/NoSQL databases, Vector DBs , and retrieval systems. Tooling : Familiarity with Haystack , Rasa , Semantic Kernel , LangChain Agents , and memory-based orchestration for agents. Applied Research : Experience integrating recent GenAI research (AutoGPT-style agents, Toolformer, etc.) into production systems. Bonus Points Contributions to open-source NLP or LLM projects. Publications in AI/NLP/ML conferences or journals. Experience in Online Reputation Management (ORM) , martech, or CX platforms. Familiarity with reinforcement learning , multi-modal AI , or few-shot learning at scale. Show more Show less

What You'll Do Design & build backend components of our MLOps platform in Python on AWS. Collaborate with geographically distributed cross-functional teams. Participate in on-call rotation with the rest of the team to handle production incidents. What You Know At least 3+ years of professional backend development experience with Python. Experience with web development frameworks such as Flask or FastAPI. Experience working with WSGI & ASGI web servers such as Gunicorn, Uvicorn etc. Experience with concurrent programming designs such as AsyncIO. Experience with containers (Docker) and container platforms like AWS ECS or AWS EKS Experience with unit and functional testing frameworks. Experience with public cloud platforms like AWS. Experience with CI/CD practices, tools, and frameworks. Nice to have skills Experience with Apache Kafka and developing Kafka client applications in Python. Experience with MLOps platorms such as AWS Sagemaker, Kubeflow or MLflow. Experience with big data processing frameworks, preferably Apache Spark. Experience with DevOps & IaC tools such as Terraform, Jenkins etc. Experience with various Python packaging options such as Wheel, PEX or Conda. Experience with metaprogramming techniques in Python. Education Bachelor’s degree in Computer Science, Information Systems, Engineering, Computer Applications, or related field. Benefits In addition to competitive salaries and benefits packages, Nisum India offers its employees some unique and fun extras: Continuous Learning - Year-round training sessions are offered as part of skill enhancement certifications sponsored by the company on an as need basis. We support our team to excel in their field. Parental Medical Insurance - Nisum believes our team is the heart of our business and we want to make sure to take care of the heart of theirs. We offer opt-in parental medical insurance in addition to our medical benefits. Activities -From the Nisum Premier League's cricket tournaments to hosted Hack-a-thon, Nisum employees can participate in a variety of team building activities such as skits, dances performance in addition to festival celebrations. Free Meals - Free snacks and dinner is provided on a daily basis, in addition to subsidized lunch. Show more Show less

Hiring for top Unicorns & Soonicorns of India! We’re looking for a Machine Learning Engineer who thrives at the intersection of data, technology, and impact. You’ll be part of a fast-paced team that leverages ML/AI to personalize learning journeys, optimize admissions, and drive better student outcomes. This role is ideal for someone who enjoys building scalable models and deploying them in production to solve real-world problems. What You’ll Do Build and deploy ML models to power intelligent features across the Masai platform — from admissions intelligence to student performance prediction. Collaborate with product, engineering, and data teams to identify opportunities for ML-driven improvements. Clean, process, and analyze large-scale datasets to derive insights and train models. Design A/B tests and evaluate model performance using robust statistical methods. Continuously iterate on models based on feedback, model drift, and changing business needs. Maintain and scale the ML infrastructure to ensure smooth production deployments and monitoring. What We’re Looking For 2–4 years of experience as a Machine Learning Engineer or Data Scientist. Strong grasp of supervised, unsupervised, and deep learning techniques. Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch, etc.). Experience with data wrangling tools like Pandas, NumPy, and SQL. Familiarity with model deployment tools like Flask, FastAPI, or MLflow. Experience working with cloud platforms (AWS/GCP/Azure) and containerization (Docker/Kubernetes) is a plus. Ability to translate business problems into machine learning problems and communicate solutions clearly. Bonus If You Have Experience working in EdTech or with personalized learning systems. Prior exposure to NLP, recommendation systems, or predictive modeling in a consumer-facing product. Contributions to open-source ML projects or publications in the space. Show more Show less