103 Ml Pipelines Jobs - Page 3

Your Role and Responsibilities 3+ years of experience in a Data Science, machine learning or a related field. Strong hands-on experience in Machine Learning and Statistics focusing on structured and unstructured data problems. Practical experience in several of the following areas: time series forecasting, clustering and classification techniques, regression, boosting algorithms, optimization techniques, NLP, recommendation systems, ElasticNet Excellent programming skills preferably in Python/Py spark and SQL Understanding of developing, implementing, deploying machine learning models on the cloud platforms(Azure, AWS, GCP) by using AWS/Azure Machine Learning, Data bricks, or other relevant cloud services Integrate machine learning models into existing systems and applications, ensuring seamless functionality and data flow Understanding of developing and maintaining MLOps pipelines for automated model training, testing, deployment, and monitoring Understanding of monitoring and analysing model performance, providing reports and insights to stakeholders as needed Familiarity with data processing and storage tools, such as SQL, Hadoop, or Spark Advanced engineering abilities to deliver flexible and scalable end-to-end machine learning solutions. Exposure to data visualization software and packages (Power BI, Tableau, matplotlib, d3) Understands challenges in business area, applicability of relevant data science disciplines, and system interactions. Excellent written and verbal communication skills, confidence in presenting ideas and findings to stakeholders, and ability to do so at the right level of detail. Required Technical and Professional Expertise Engineering Graduate from a reputed institute and/or Masters in Statistics, MBA 3+ years of Data science experience Strong expertise and deep understanding of machine learning. Strong understanding of SQL & Python. Knowledge of Power BI or Tableau is a plus Exposure to Industry specific (CPG, Manufacturing) use cases is required Strong client-facing skills Must be organized and detail oriented. Excellent communication and interpersonal skills Preferred Technical and Professional Experience Strong foundation in Supervised and Unsupervised Learning (Regression, Classification, Clustering, etc.). Proficiency in Ensemble Learning (Random Forest, Gradient Boosting, XGBoost, LightGBM, etc.). Experience in fine-tuning Large Language Models (LLMs) and working with open-source models (Llama, GPT, BERT, etc.). Familiarity with Prompt Engineering, RAG (Retrieval-Augmented Generation), and Fine-tuning techniques. Hands-on experience with Cloud Platforms (AWS, GCP, Azure) for ML model deployment. Familiarity with MLOps and Model Deployment using Kubernetes, Docker, and MLflow.

Machine Learning Engineer About Caliper: Caliper is an AI-enabled, comprehensive value-based care (VBC) risk analytics suite designed to provide affordable, accessible and actionable insights. Our solution empowers Community Care Providers to thrive in the VBC landscape, ensuring improved patient outcomes, operational efficiency and financial success. Position: Machine Learning Engineer (with NLP & AWS Experience) We are hiring a Machine Learning Engineer who brings strong foundational skills across ML workflows, with a working focus on Natural Language Processing (NLP) and experience deploying ML systems on AWS. This role is ideal for someone who is technically versatilecomfortable working across diverse machine learning problems including NLP, classification, forecasting, embeddings, and recommender systemswhile being hands-on with modern ML tooling, model lifecycle management, and cloud infrastructure. Key Responsibilities: Build and deploy ML models for a variety of use cases such as classification, prediction, NLP tasks, and recommender systems. Design, implement, and maintain end-to-end ML pipelines, including data preprocessing, model training, validation, and deployment. Apply NLP techniques where applicable (e.g., sentiment analysis, NER, document parsing, embeddings). Leverage AWS services (e.g., SageMaker, Lambda, S3, Bedrock, Comprehend) to deploy and scale ML solutions in production. Participate in model evaluation, monitoring, and retraining workflows. Collaborate with product, data, and engineering teams to understand requirements and translate them into ML-driven solutions. Support both experimental research and production-grade deployment workstreams. Required Skills & Experience: 4+ years of experience as a Machine Learning Engineer or Applied Scientist. Strong hands-on experience in core ML techniques: regression, classification, clustering, tree-based models, embeddings, etc. Solid Python programming skills and experience with libraries like Scikit-learn, PyTorch or TensorFlow, Pandas, NumPy. Exposure to NLP models and libraries (e.g., Hugging Face Transformers, spaCy, NLTK) with practical application experience. Experience deploying models using AWS cloud infrastructure, particularly SageMaker, Comprehend, Lambda, or Bedrock. Comfortable with model evaluation, metrics (e.g., accuracy, ROC-AUC, F1), and debugging pipelines in production. Experience working with version control, CI/CD tools, and basic MLOps practices. Nice to Have: Familiarity with Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., FAISS, Milvus, Weaviate). Knowledge of prompt engineering or foundation model tuning (e.g., OpenAI, Claude, Bedrock). Experience with time series models, anomaly detection, or customer intelligence use cases. Exposure to Docker, Kubernetes, or Airflow for workflow orchestration. What We are Looking For: A generalist ML engineer who can adapt to evolving problem statements across NLP, tabular, or other ML use cases. Someone who balances code quality and experimentation, and can own model delivery end-to-end. A collaborative team player who is curious, self-driven, and excited to build in a fast-paced environment. Compensation will be commensurate with experience. If you are interested, please send your application to jobs@precognitas.com and For more information about our work, visit www.caliper.care

IMMEDIATE JOINERS ONLY Job Title: Senior MLOps Engineer Location : NCR Location (WFO) Note: DevOps -Knowledge is fine Experience Range: 6-12 years Primary Key skills: MLOps Key Responsibilities: Design, develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, maintenance, and scalability. Automate the retraining, testing, and validation processes for ML models. Collaborate with cross-functional teams, including data science, software engineering and DevOps, to integrate ML models into production systems. Monitor model performance, diagnose issues, and implement improvements. Ensure scalability, reliability, and compliance of ML systems in production. Optimize infrastructure costs while maintaining high system performance. Stay up-to-date with the latest developments in MLOps, machine learning and AI, and apply new techniques to improve existing models and processes. Qualifications: Education: Bachelors or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field. Experience: Solid experience as a MLOps Engineer, Machine Learning Engineer, DevOps Engineer, or similar role. Experience in the retail industry or e-commerce is highly desirable. Technical Skills: Strong experience with Infrastructure as Code frameworks and languages (Terraform, Bicep or ARM) Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch, etc.). Hands-on experience with containerization (Docker) and orchestration tools (Kubernetes). Proficiency in CI/CD tools and cloud platforms (AWS, Azure, or Google Cloud). Knowledge of model monitoring and evaluation metrics. Familiarity with version control systems, such as Git, and model versioning tools like MLflow or DVC. Experience with Generative AI product deployment is desirable. Experience with big data using Databricks, Snowflake, Apache Spark or Hadoop is desirable. – some of these System level architecture understanding including scaling, MLOps, model/data monitoring, andensuring a deterministic pipeline. Soft Skills: Strong problem-solving skills with the ability to work independently and collaboratively in a fast-paced environment. Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. A proactive attitude and a passion for continuous learning and innovation.

Precognitas Health Pvt. Ltd., a fully owned subsidiary of Foresight Health Solutions LLC, is seeking a Data Engineer to build and optimize our data pipelines, processing frameworks, and analytics infrastructure that power critical healthcare insights. Are you a bright, energetic, and skilled data engineer who wants to make a meaningful impact in a dynamic environment? Do you enjoy designing and implementing scalable data architectures, ML pipelines, automating ETL workflows, and working with cloud-native solutions to process large datasets efficiently? Are you passionate about transforming raw data into actionable insights that drive better healthcare outcomes? If so, join us! Youll play a crucial role in shaping our data strategy, optimizing data ingestion, and ensuring seamless data flow across our systems while leveraging the latest cloud and big data technologies. Required Skills & Experience : 4+ years of experience in data engineering, data pipelines, and ETL/ELT workflows. Strong Python programming skills with expertise in Python Programming, NumPy, Pandas, and data manipulation techniques. Hands-on experience with orchestration tools like Prefect, Apache Airflow, or AWS Step Functions for managing complex workflows. Proficiency in AWS services, including AWS Glue, AWS Batch, S3, Lambda, RDS, Athena, and Redshift. Experience with Docker containerization and Kubernetes for scalable and efficient data processing. Strong understanding of data processing layers, batch and streaming data architectures, and analytics frameworks. Expertise in SQL and NoSQL databases, query optimization, and data modeling for structured and unstructured data. Familiarity with big data technologies like Apache Spark, Hadoop, or similar frameworks. Experience implementing data validation, quality checks, and observability for robust data pipelines. Strong knowledge of Infrastructure as Code (IaC) using Terraform or AWS CDK for managing cloud-based data infrastructure. Ability to work with distributed systems, event-driven architectures (Kafka, Kinesis), and scalable data storage solutions. Experience with CI/CD for data workflows, including version control (Git), automated testing, and deployment pipelines. Knowledge of data security, encryption, and access control best practices in cloud environments. Strong problem-solving skills and ability to collaborate with cross-functional teams, including data scientists and software engineers. Compensation will be commensurate with experience. If you are interested, please send your application to jobs@precognitas.com. For more information about our work, visit www.caliper.care

Role: AI/ML Lead Engineer Location: Ambattur, Chennai(Onsite) Fulltime Position Job Summary: We are looking for an AI/ML Engineer to Lead, develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines , collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting . This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization. Key Responsibilities: Design, build, and optimize machine learning models for various business applications. Develop and maintain ML pipelines , including data preprocessing, feature engineering, and model training. Work with TensorFlow, PyTorch, Scikit-learn, and Keras for model development. Deploy ML models in cloud environments (AWS, Azure, GCP) and work with Docker/Kubernetes for containerization. Perform model evaluation, hyperparameter tuning, and performance optimization . Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions. Stay up to date with the latest advancements in AI/ML and implement best practices. Write clean, scalable, and well-documented code in Python or R. Technical Skills: Programming Languages: Proficiency in languages like Python. Python is particularly popular for developing ML models and AI algorithms due to its simplicity and extensive libraries like NumPy, Pandas, and Scikit-learn. Machine Learning Algorithms: Should have a deep understanding of supervised learning (linear regression, decision trees, SVM), unsupervised learning, and reinforcement learning. Data Management and Analysis: Skills in data cleaning, feature engineering, and data transformation are crucial. Deep Learning: Familiarity with neural networks, CNNs, RNNs, and other architectures is important. Machine Learning Frameworks and Libraries: Experience with TensorFlow, PyTorch, Keras, or Scikit-learn is valuable. Natural Language Processing (NLP): Familiarity with NLP techniques like word2vec, sentiment analysis, and summarization can be beneficial. Cloud Computing: Experience with cloud-based services like AWS SageMaker, Google Cloud AI Platform, or Microsoft Azure Machine Learning. Data Preprocessing: Skills in handling missing data, data normalization, feature scaling, and data transformation. Feature Engineering: Ability to create new features from existing data to improve model performance. Data Visualization: Familiarity with visualization tools like Matplotlib, Seaborn, Plotly, or Tableau. Containerization: Knowledge of containerization tools like Docker and Kubernetes. Databases : Understanding of relational databases (e.g., MySQL) and NoSQL databases (e.g., MongoDB). Data Warehousing: Familiarity with data warehousing concepts and tools like Amazon Redshift or Google BigQuery. Computer Vision: Understanding of computer vision concepts and techniques like object detection, segmentation, and image classification.

Role: Midlevel AI/ML Engineer Location: Ambattur, Chennai(Onsite) Fulltime Position Job Summary: We are looking for an AI/ML Engineer to develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines , collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting . This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization. Key Responsibilities: Design, build, and optimize machine learning models for various business applications. Develop and maintain ML pipelines , including data preprocessing, feature engineering, and model training. Work with TensorFlow, PyTorch, Scikit-learn, and Keras for model development. Deploy ML models in cloud environments (AWS, Azure, GCP) and work with Docker/Kubernetes for containerization. Perform model evaluation, hyperparameter tuning, and performance optimization . Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions. Stay up to date with the latest advancements in AI/ML and implement best practices. Write clean, scalable, and well-documented code in Python or R.

What will your role look like Design, train, evaluate, and deploy traditional ML models as well as Generative AI-based applications. Work on supervised, unsupervised, and deep learning models including regression, classification, clustering, and sequence models. Build end-to-end ML pipelines including data preprocessing, feature engineering, model training, hyperparameter tuning, and evaluation. Develop and optimize LLM-based workflows using LangChain, LangGraph, and orchestration frameworks. Fine-tune, evaluate, and integrate LLMs such as GPT, LLaMA, Claude, Mistral, Falcon, Cohere, and Gemini. Implement Retrieval-Augmented Generation (RAG) using embeddings and vector stores like FAISS, Pinecone, or Chroma. Apply prompt engineering, LoRA, PEFT, and adapter-based fine-tuning to optimize LLMs for specific tasks. Build Agentic AI systems with tool-use capabilities and reasoning chains (e.g., ReAct, AutoGPT, BabyAGI, CrewAI). Use Hugging Face for leveraging pre-trained models and datasets for rapid experimentation. Collaborate with product, data, and engineering teams to productionize AI solutions using scalable cloud infrastructure. Why you will love this role Besides a competitive package, an open workspace full of smart and pragmatic team members, with ever-growing opportunities for professional and personal growth Be a part of a learning culture where teamwork and collaboration are encouraged, diversity is valued and excellence, compassion, openness and ownership is rewarded. We would like you to bring along Strong grasp of core ML concepts such as model selection, evaluation metrics, bias/variance tradeoff, overfitting/underfitting, etc. Good exposure on using Azure Open AI and hosting applications in Azure environment Experience in building and tuning models using scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Experience in building ML pipelines with feature engineering, model tuning, cross-validation, and A/B testing. Proficiency in LangChain, LangGraph, and integrating with Hugging Face Transformers Ecosystem. Deep knowledge of various LLMs and techniques like prompt engineering, few-shot learning, and instruction tuning. Experience in building Agentic AI systems and coordinating multi-agent flows or tool-chaining. Familiarity with LLMOps/MLOps tools (e.g., MLflow, Weights & Biases, Kubeflow, SageMaker, or Vertex AI). Strong programming skills in Python, and experience deploying models using FastAPI, Flask, or Streamlit. Experience with cloud platforms (AWS/GCP/Azure) and handling GPU/TPU resources. Solid understanding of data structures, algorithms, and software engineering best Practices. Highly skilled AI/ML Engineer with a solid foundation in Machine Learning and deep hands-on experience in Generative AI (GenAI). Strong capabilities in building, training, and deploying ML models, along with significant experience. working with frameworks such as LangChain, LangGraph, and platforms like Hugging Face, vector databases, and various LLMs. Youll be a key contributor in developing smart assistants, AI agents, and ML solutions that solve complex business problems. Experience with LangSmith, PromptLayer, or other LLM observability tools Familiarity with Guardrails.AI, semantic caching, and output validation techniques Exposure to multi-modal models like CLIP, DALLE, Stable Diffusion, or Whisper Contributions to open-source GenAI or ML libraries/projects Domain expertise in areas like healthcare, finance, manufacturing, or legal tech.

Educational Bachelor of Engineering Service Line Data & Analytics Unit Responsibilities Responsible for successful delivery of MLOps solutions and services in client consulting environments; Define key business problems to be solved; formulate high level solution approaches and identify data to solve those problems, develop, analyze/draw conclusions and present to client. Assist clients with operationalization metrics to track performance of ML Models Agile trained to manage team effort and track through JIRA High Impact Communication- Assesses the target audience need, prepares and practices a logical flow, answers audience questions appropriately and sticks to timeline. Additional Responsibilities: Master’s degree in Computer Science Engineering, with Relevant experience in the field of MLOps / Cloud Domain experience in Capital Markets, Banking, Risk and Compliance etc. Exposure to US/ overseas markets is preferred Azure Certified – DP100, AZ/AI900 Domain / Technical / Tools KnowledgeObject oriented programming, coding standards, architecture & design patterns, Config management, Package Management, Logging, documentation Experience in Test Driven Development and experience in using Pytest frameworks, git version control, Rest APIs Azure ML best practices in environment management, run time configurations (Azure ML & Databricks clusters), alerts. Experience designing and implementing ML Systems & pipelines, MLOps practices Exposure to event driven orchestration, Online Model deployment Contribute towards establishing best practices in MLOps Systems development Proficiency with data analysis tools (e.g., SQL, R & Python) High level understanding of database concepts/reporting & Data Science concepts Hands on experience in working with client IT/Business teams in gathering business requirement and converting into requirement for development team Experience in managing client relationship and developing business cases for opportunities Azure AZ-900 Certification with Azure Architecture understanding is a plus Technical and Professional : Technical knowledge- has expertise in cloud technologies, specifically MS Azure, and services with hands on coding to – Python Programming - Expert and Experienced - 4 -5 years DevOps Working knowledge with implementation experience - 1 or 2 projects a minimum Hands-On MS Azure Cloud knowledge Understand and take requirements on Operationalization of ML Models from Data Scientist Help team with ML Pipelines from creation to execution List Azure services required for deployment, Azure Data bricks and Azure DevOps Setup Assist team to coding standards (flake8 etc) Guide team to debug on issues with pipeline failures Engage with Business / Stakeholders with status update on progress of development and issue fix Automation, Technology and Process Improvement for the deployed projects Setup Standards related to Coding, Pipelines and Documentation Adhere to KPI / SLA for Pipeline Run, Execution Research on new topics, services and enhancements in Cloud Technologies Preferred Skills: Technology-Machine Learning-Python

Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : Machine Learning Operations Good to have skills : NAMinimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Summary :As an Machine Learning Engineer/MLOps Expert, you will engage in the operationalization of Machine Learning Models that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready ML system, ensuring high-quality standards are met. Roles & Responsibilities:- Continuously evaluate and improve existing processes to enhance efficiency.- Engage with multiple teams and contribute on key decisions.- Provide solutions to problems for their immediate team and across multiple teams.- Facilitate knowledge sharing sessions to enhance team skills and capabilities.- Monitor project progress and ensure alignment with strategic goals. Professional & Technical Skills: - ML Pipeline Development:Design, build, and maintain scalable pipelines for model training to support our AI initiatives.- Model Deployment & Serving:Deploy machine learning models as robust, secure services containerize models with Docker and serve them via FastAPI on AWS ensuring low-latency predictions for marketing applications. Manage Batch inference and Realtime inference.- CI/CD Automation:Implement continuous integration and delivery (CI/CD) pipelines for ML projects. Automate testing, model validation, and deployment workflows using tools like GitHub Actions to accelerate delivery.- Model Lifecycle Management:Orchestrate the end-to-end ML lifecycle, including versioning, packaging, and registering models. Maintain a model repository/registry (MLflow or similar) for reproducibility and governance from experimentation through production. Experience on MLFlow and Airflow is mandatory- Monitoring & Optimization:Monitor model performance, data drift, and system health in production. Set up alerts and dashboards and proactively initiate model retraining or tuning to sustain accuracy and efficiency over time.- Must To Have Skills: Proficiency in Machine Learning Operations.- Strong understanding of cloud-based AI services and deployment strategies.- Should have Multi Cloud skills- Experience with Machine learning frameworks- Ability to implement and optimize machine learning models for production environments. Additional Information:- The candidate should have minimum 7.5 years of experience in Machine Learning Operations.- This position is based at our Bengaluru office.- A 15 years full time education is required. Qualification 15 years full time education

Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : Machine Learning Operations, Java Good to have skills : NAMinimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Summary :As an Machine Learning Engineer/MLOps Expert, you will engage in the operationalization of Machine Learning Models that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready ML system, ensuring high-quality standards are met. Roles & Responsibilities:- Continuously evaluate and improve existing processes to enhance efficiency.- Engage with multiple teams and contribute on key decisions.- Provide solutions to problems for their immediate team and across multiple teams.- Facilitate knowledge sharing sessions to enhance team skills and capabilities.- Monitor project progress and ensure alignment with strategic goals. Professional & Technical Skills: - ML Pipeline Development:Design, build, and maintain scalable pipelines for model training to support our AI initiatives.- Model Deployment & Serving:Deploy machine learning models as robust, secure services containerize models with Docker and serve them via FastAPI on AWS ensuring low-latency predictions for marketing applications. Manage Batch inference and Realtime inference.- CI/CD Automation:Implement continuous integration and delivery (CI/CD) pipelines for ML projects. Automate testing, model validation, and deployment workflows using tools like GitHub Actions to accelerate delivery.- Model Lifecycle Management:Orchestrate the end-to-end ML lifecycle, including versioning, packaging, and registering models. Maintain a model repository/registry (MLflow or similar) for reproducibility and governance from experimentation through production. Experience on MLFlow and Airflow is mandatory- Monitoring & Optimization:Monitor model performance, data drift, and system health in production. Set up alerts and dashboards and proactively initiate model retraining or tuning to sustain accuracy and efficiency over time.- Must To Have Skills: Proficiency in Machine Learning Operations.- Strong understanding of cloud-based AI services and deployment strategies.- Should have Multi Cloud skills- Experience with Machine learning frameworks- Ability to implement and optimize machine learning models for production environments. Additional Information:- The candidate should have minimum 5 years of experience in Machine Learning Operations.- This position is based at our Bengaluru office.- A 15 years full time education is required. Qualification 15 years full time education

Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : Machine Learning Operations, Manual Testing Good to have skills : NAMinimum 5 year(s) of experience is required Educational Qualification : 15 years full time education Summary :As an Machine Learning Engineer/MLOps Expert, you will engage in the operationalization of Machine Learning Models that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready ML system, ensuring high-quality standards are met. Roles & Responsibilities:- Continuously evaluate and improve existing processes to enhance efficiency.- Engage with multiple teams and contribute on key decisions.- Provide solutions to problems for their immediate team and across multiple teams.- Facilitate knowledge sharing sessions to enhance team skills and capabilities.- Monitor project progress and ensure alignment with strategic goals. Professional & Technical Skills: - ML Pipeline Development:Design, build, and maintain scalable pipelines for model training to support our AI initiatives.- Model Deployment & Serving:Deploy machine learning models as robust, secure services containerize models with Docker and serve them via FastAPI on AWS ensuring low-latency predictions for marketing applications. Manage Batch inference and Realtime inference.- CI/CD Automation:Implement continuous integration and delivery (CI/CD) pipelines for ML projects. Automate testing, model validation, and deployment workflows using tools like GitHub Actions to accelerate delivery.- Model Lifecycle Management:Orchestrate the end-to-end ML lifecycle, including versioning, packaging, and registering models. Maintain a model repository/registry (MLflow or similar) for reproducibility and governance from experimentation through production. Experience on MLFlow and Airflow is mandatory- Monitoring & Optimization:Monitor model performance, data drift, and system health in production. Set up alerts and dashboards and proactively initiate model retraining or tuning to sustain accuracy and efficiency over time.- Must To Have Skills: Proficiency in Machine Learning Operations.- Strong understanding of cloud-based AI services and deployment strategies.- Should have Multi Cloud skills- Experience with Machine learning frameworks- Ability to implement and optimize machine learning models for production environments. Additional Information:- The candidate should have minimum 5 years of experience in Machine Learning Operations.- This position is based at our Bengaluru office.- A 15 years full time education is required. Qualification 15 years full time education

About the Role: We are seeking a visionary AI Architect to drive our AI/ML and GenAI roadmap. You will lead the design of multi-modal generative workflows that combine image, video, and LLM-driven tasks for media workflow automation. This role will influence our core platform strategy by integrating custom and open-source diffusion models, prompt-to-image generation, segmentation, and agentic task orchestration using LLMs. Key Responsibilities: Architect AI solutions for image/video generation, segmentation, pose estimation, and retouching using diffusion models and GANs. Design agentic workflows leveraging LLMs (OpenAI, Claude, etc.) for orchestrating creative task chains, model prompting, or hybrid AI-human loops. Lead data pipeline and preparation strategy define data requirements, build annotation pipelines, and guide synthetic data generation. Evaluate and fine-tune foundation models like Stable Diffusion, ControlNet, OpenPose, DeepFaceLab, CP-VTON. Guide research, benchmarking, fine-tuning, and deployment of models. Evaluate build vs. buy decisions for third-party APIs or in-house model development. Ensure compliance with IP, client data protection, fairness, and ethical AI practices. Desired Skills: Deep experience in computer vision, generative AI (diffusion, transformers), and LLM integration Hands-on with PyTorch, Hugging Face, Diffusion/ GAN Architectures Hands-on with LLM and agentic frameworks like LangChain, AWS Bedrock Strong understanding of synthetic data pipelines, model evaluation metrics, and prompt engineering Strong understanding of image harmonization, segmentation, texture transfer Ability to architect scalable ML pipelines combining image/video generation with LLM reasoning Experience leading a technical team or managing AI initiatives end-to-end. Familiarity with AWS/GCP, GPU infrastructure, and scalable AI model serving Familiarity with co-pilot tools and AI-assisted code generation to accelerate prototyping and implementation of AI systems Desired Profile & Qualification: 8+ years in AI/ML roles including leadership in GenAI or CV-heavy platforms Degree in Computer Science, Machine Learning, or related field (MS/PhD preferred). Prior contributions to open-source AI or research publications is an advantage Note: This is an on-site position.

RESPONSIBILITIES: Develop, productionize, and deploy scalable, resilient software solutions for operationalizing AI & ML. Deploy Machine Learning (ML) models and Large Language Models (LLM) securely and efficiently, both in the cloud and on-premises, using state of the art platforms, tools, and techniques. Provide effective model observability, monitoring, and metrics by instrumenting logging, dashboards, alerts, etc. In collaboration with Data Engineers, design and build pipelines for extraction, transformation, and loading of data from a variety of data sources for AI & ML models as well as RAG architectures for LLMs. Enable Data Scientists to work more efficiently by providing tools for experiment tracking and test automation. Ensure scalability of built solutions by developing and running rigorous load tests. Facilitate integration of AI & ML capabilities into user experience by building APIs, UIs, etc. Stay current on new developments in AI & ML frameworks, tools, techniques, and architectures available for solution development, both private and open source. Coach data scientists and data engineers on software development best practices to write scalable, maintainable, well-designed code. Agile Project Work Work in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, DevOps engineers, designers, product managers, technical delivery teams, and others to continuously innovate AI and MLOps solutions. Act as a positive champion for broader organization to develop stronger understanding of software design patterns that deliver scalable, maintainable, well-designed analytics solutions. Advocate for security and responsibility best practices and tools. Acts as an expert on complex technical topics that require cross-functional consultation. Perform other duties as required. QUALIFICATIONS: Experience applying continuous integration/continuous delivery best practices, including Version Control, Trunk Based Development, Release Management, and Test-Driven Development Experience with popular MLOps tools (e.g., Domino Data Labs, Dataiku, mlflow, AzureML, Sagemaker), and frameworks (e.g.: TensorFlow, Keras, Theano, PyTorch, Caffe, etc.) Experience with LLM platforms (OpenAI, Bedrock, NVAIE) and frameworks (LangChain, LangFuse, vLLM, etc.) Experience in programming languages common to data science such as Python, SQL, etc. Understanding of LLMs, and supporting concepts (tokenization, guardrails, chunking, Retrieval Augmented Generation, etc.). Knowledge of ML lifecycle (wrangling data, model selection, model training, modeling validation and deployment at scale) and experience working with data scientists Familiar with at least one major cloud provider (Azure, AWS, GCP), including resource provisioning, connectivity, security, autoscaling, IaC. Familiar with cloud data warehousing solutions such as Snowflake, Fabric, etc. Experience with Agile and DevOps software development principles/methodologies and working on teams focused on delivering business value. Experience influencing and building mindshare convincingly with any audience. Confident and experienced in public speaking. Ability to communicate complex ideas in a concise way. Fluent with popular diagraming and presentation software. Demonstrated experience in teaching and/or mentoring professionals.

We are looking for an experienced Machine Learning Engineer to lead the development and deployment of ML models that power intelligent products and insights. You will collaborate with teams across Data Science, Engineering, and Product to build solutions that are scalable, efficient, and impactful. Candidates with exposure to the healthcare domain are encouraged to apply, although this is not a mandatory requirement . Key Responsibilities: Architect, build, and maintain end-to-end ML systems from data pipelines to model deployment. Develop and optimize machine learning models for use cases such as classification, prediction, recommendation, NLP, or computer vision. Implement MLOps best practices for model training, tracking, deployment, and monitoring. Collaborate with data scientists and domain experts to productionize prototypes and research. Evaluate and monitor model performance; ensure robustness, fairness, and explainability. Document architecture and processes; contribute to knowledge sharing and code reviews. (Preferred) Work with EHR data, claims data, clinical notes, or healthcare interoperability formats like HL7 or FHIR, if applicable. Required Skills & Qualifications: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related discipline. 5+ years of hands-on experience in ML engineering or applied data science. Strong command of Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost). Experience deploying ML models in production using containerization (Docker, Kubernetes) and cloud platforms (AWS/GCP/Azure). Familiarity with MLOps tools like MLflow, DVC, or Kubeflow. Proficient in building ETL/ELT pipelines and handling large-scale datasets. Strong understanding of statistical methods, model evaluation metrics, and optimization techniques. Good software engineering practices (version control, testing, CI/CD). Preferred Qualifications: Exposure to healthcare datasets such as medical claims, EHR/EMR, HL7, FHIR, or medical coding (CPT, ICD-10). Experience with NLP models applied to clinical documentation or unstructured medical data. Understanding of HIPAA compliance, data anonymization, and PHI handling. Contributions to open-source ML projects or peer-reviewed publications. Experience working in regulated industries or mission-critical environments.

We're hiring experienced professionals in our growing Data science Practice at EY GDS Hyderabad Office . We are looking for talented professionals to join our team. Below are the details for the open positions: Position : AI and DATA -ML Ops Engineers Experience : 4-12 years Location : Hyderabad Programming Languages: Proficiency in Python (3.x) and SQL. ML Frameworks & Libraries: Extensive knowledge of ML frameworks (TensorFlow, PyTorch, Scikit-learn), data structures, data modelling, and software architecture. Databases: Experience with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, DynamoDB) databases. Mathematics & Algorithms: Strong understanding of mathematics, statistics, and algorithms for machine learning applications. ML Modules & REST API: Experience in developing and integrating ML modules with RESTful APIs. Version Control: Hands-on experience with Git and best practices for version control. Model Deployment & Monitoring: Experience in deploying and monitoring ML models using: MLflow (for model tracking, versioning, and deployment) WhyLabs (for model monitoring and data drift detection) Kubeflow (for orchestrating ML workflows) Airflow (for managing ML pipelines) Docker & Kubernetes (for containerization and orchestration) Prometheus & Grafana (for logging and real-time monitoring) Data Processing: Ability to process and transform unstructured data into meaningful insights (e.g., auto-tagging images, text-to-speech conversions) Apply Here: https://careers.ey.com/job-invite/1606302/ Please share the updated resume to Gayathri.s8@gds.ey.com . We look forward to discussing your potential with EY!

Dear Candidate, We are seeking a Machine Learning Engineer to develop predictive models and deploy them into production. Ideal for professionals passionate about AI and data science. Key Responsibilities: Develop and train machine learning models Preprocess and analyze large datasets Deploy models using scalable infrastructure Collaborate with product teams to integrate ML solutions Required Skills & Qualifications: Strong knowledge of Python and ML libraries (scikit-learn, TensorFlow, PyTorch) Experience with data preprocessing and feature engineering Familiarity with model deployment techniques Bonus: Experience with cloud ML services (AWS SageMaker, Google AI Platform) Soft Skills: Strong troubleshooting and problem-solving skills. Ability to work independently and in a team. Excellent communication and documentation skills. Note: If interested, please share your updated resume and preferred time for a discussion. If shortlisted, our HR team will contact you. Kandi Srinivasa Delivery Manager Integra Technologies

Design, develop, and deploy AI/ML models; build scalable, low-latency ML infrastructure; run experiments; optimize algorithms; collaborate with data scientists, engineers, and architects; integrate models into production to drive business value. Required Candidate profile 5–10 yrs in AI/ML, strong in model development, optimization, and deployment. Skilled in Azure, ML pipelines, data science tools, and collaboration with cross-functional teams.

This is an urgent and fast filling position - Need immediate joiners OR l1 month notice period We are Looking for 1)Junior AI/ML Engineer - Positions 2 open 2)Mid-level AI/ML Engineer -1 position open 3)Lead AI/ML Engineer - 1 position open Location: Ambattur, Chennai Fulltime position Job Summary: We are looking for a AI/ML Engineer to develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines , collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting . This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization. Key Responsibilities: Design, build, and optimize machine learning models for various business applications. Develop and maintain ML pipelines , including data preprocessing, feature engineering, and model training. Work with TensorFlow, PyTorch, Scikit-learn, and Keras for model development. Deploy ML models in cloud environments (AWS, Azure, GCP) and work with Docker/Kubernetes for containerization. Perform model evaluation, hyperparameter tuning, and performance optimization . Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions. Stay up to date with the latest advancements in AI/ML and implement best practices. Write clean, scalable, and well-documented code in Python or R. Technical Skills: Programming Languages: Proficiency in languages like Python. Python is particularly popular for developing ML models and AI algorithms due to its simplicity and extensive libraries like NumPy, Pandas, and Scikit-learn. Machine Learning Algorithms: Should have a deep understanding of supervised learning (linear regression, decision trees, SVM), unsupervised learning, and reinforcement learning. Data Management and Analysis: Skills in data cleaning, feature engineering, and data transformation are crucial. Deep Learning: Familiarity with neural networks, CNNs, RNNs, and other architectures is important. Machine Learning Frameworks and Libraries: Experience with TensorFlow, PyTorch, Keras, or Scikit-learn is valuable. Natural Language Processing (NLP): Familiarity with NLP techniques like word2vec, sentiment analysis, and summarization can be beneficial. Cloud Computing: Experience with cloud-based services like AWS SageMaker, Google Cloud AI Platform, or Microsoft Azure Machine Learning. Data Preprocessing: Skills in handling missing data, data normalization, feature scaling, and data transformation. Feature Engineering: Ability to create new features from existing data to improve model performance. Data Visualization: Familiarity with visualization tools like Matplotlib, Seaborn, Plotly, or Tableau. Containerization: Knowledge of containerization tools like Docker and Kubernetes. Databases : Understanding of relational databases (e.g., MySQL) and NoSQL databases (e.g., MongoDB). Data Warehousing: Familiarity with data warehousing concepts and tools like Amazon Redshift or Google BigQuery. Computer Vision: Understanding of computer vision concepts and techniques like object detection, segmentation, and image classification. Reinforcement Learning: Knowledge of reinforcement learning concepts and techniques like Q-learning and policy gradients.

Role Summary: We are seeking an experienced and talented Senior Engineer to join our MLOps team. In this role, you will play a crucial part in designing, developing, and maintaining scalable and reliable machine learning operations (MLOps) pipelines and infrastructure. You will collaborate closely with data scientists, software engineers, and other stakeholders to ensure the successful deployment and monitoring of machine learning models in production environments. Responsibilities: Design and implement robust MLOps pipelines for model training, evaluation, deployment, and monitoring using industry-standard tools and frameworks Collaborate with data scientists to streamline the model development process and ensure seamless integration with MLOps pipelines. Optimize and scale machine learning infrastructure to support high-performance model training and inference. Contribute to the development of MLOps standards, processes, and documentation within the organization. Mentor and support junior team members in MLOps practices and technologies. Stay up-to-date with the latest trends and best practices in MLOps, and explore opportunities for continuous improvement. Qualifications: Bachelor's or Master's degree in Computer Science, Statistics, or a related field. 5+ years of experience in software engineering, with 2+ years experience in ML Proficient in Python and at least one other programming language (e.g., Java, Go, C++). Extensive experience with containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure). Familiarity with machine learning frameworks and MLOps tools Experience with big data technologies Strong understanding of CI/CD principles and practices. Preferred Qualifications: Familiarity with model serving frameworks Knowledge of infrastructure as code (IaC) tools Experience with monitoring and observability tools Contributions to open-source MLOps projects or communities.

About Aplazo Aplazo is a Mexican BNPL startup redefining financial access for the underbanked. Unlike its global counterparts, Aplazo isnt just about debtits an alternative to cash, offering fair, simple, and transparent financial solutions. Founded four years ago, Aplazo enables users to split payments online and in-store without a credit card, empowering financial freedom and opportunity across Latin America. Our tech-driven approach minimizes credit loss while ensuring accessibility—even for the 40% of users with no credit history. With in-store transactions making up more than half of our business, we bridge the gap in Mexico’s evolving financial landscape. Merchants benefit from increased basket sizes, higher conversions, and stronger customer engagement. Backed by $110M in funding , Aplazo is poised for continued innovation. We’re building Latin America’s most beloved fintech and are seeking passionate technologists and leaders who thrive on collaboration, quality, and impact . Aplazo on TechCrunch : https://techcrunch.com/2024/05/13/aplazo/ About Data Science @ Aplazo The Data Science team at Aplazo is a strategic driver of innovation and transformation. With a strong product-first mindset and deep technical expertise, we solve complex problems across risk, payments, personalization, fraud detection, marketing, customer lifecycle, recommendations, underwriting, and more. A cornerstone of our success is our robust MLOps infrastructure —featuring automated CI/CD pipelines, model and data versioning, and comprehensive observability to support a seamless end-to-end ML lifecycle. Now, we are investing in next-generation MLOps capabilities to further scale and future-proof our systems. Role Overview We are seeking a visionary Lead/Staff MLOps Engineer to lead the evolution of our ML infrastructure. This is a high-impact role for a technical leader who thrives on building scalable platforms, accelerating experimentation workflows, and enabling high-velocity AI development. You’ll define the MLOps roadmap, establish best practices, and build resilient systems that empower our Data Science and Engineering teams to operate at scale. You will work closely with stakeholders across Product, Growth, Engineering, and Data to translate complex business goals into reliable, high-performing machine learning systems. Key Responsibilities Architect scalable ML systems with a focus on reliability, security, automation, and performance Lead the end-to-end MLOps strategy : CI/CD for ML, model registries, feature stores, testing, deployment, and monitoring Drive innovation across ML domains (LLMs, NLP, personalization, fraud detection, pricing, customer science) Optimize ML workflows for cost, latency, reproducibility , and resource efficiency Define rigorous model governance standards including auditability, reproducibility, versioning, rollback mechanisms Evaluate and integrate new technologies (LLMOps, Foundation Models, LangChain, etc.) through structured POCs Serve as technical mentor and thought leader , influencing teams and instilling engineering excellence Partner with executive leadership on quarterly OKRs aligned to risk-adjusted growth, profitability, and model performance Collaborate across geographies—Mexico, USA, Chile, and Europe—to ensure strategic alignment Required Qualifications Experience 6+ years in MLOps, ML Engineering, or Software Engineering, with 2+ years in a senior leadership role Proven success in building and scaling production-grade ML platforms Strong exposure to cloud-native infrastructure (GCP or AWS preferred) Experience deploying AI/GenAI systems in regulated environments Technical Skills Expert in Python and ML stack (TensorFlow, PyTorch, Scikit-learn, LangChain, OpenAI APIs) CI/CD tools (GitHub Actions, Argo, Kubeflow, MLflow) Kubernetes, Docker, ONNX, TorchServe for model serving and orchestration Strong with data warehousing and processing tools (BigQuery, Snowflake, Spark, Kafka, Flink) Experience with metadata management , feature stores , model versioning , A/B testing , and monitoring systems Familiarity with LLMOps, DataOps (Airflow, dbt), and streaming architectures Soft Skills Exceptional leadership and mentoring skills Excellent written and verbal communication Ability to work independently and cross-functionally in a fast-paced environment Preferred Qualifications Bachelor’s or Master’s in Computer Science, Statistics, or related field (Tier-I institutions preferred) PhD in Data Science, Machine Learning, or related field (with 8+ years of relevant experience) Publications or conference presentations in ML/AI/DS fields Spanish language proficiency is a plus Nice to Have Experience in fintech, risk, fraud, or payments Exposure to model fairness, explainability, and responsible AI frameworks Familiarity with LLMOps stacks (OpenLLM, LangChain, Guardrails) and prompt engineering for GPT-based models Languages English: Advanced proficiency Spanish: Nice to have Why Join Us Competitive salary and equity Remote-first flexibility + in-person offsites Annual learning budget + global conference participation Ownership-driven culture, fast iteration cycles, low bureaucracy

We're Hiring: Data Scientist Databricks & ML Deployment Expert Location: Mumbai/Pune Experience: 38 Years Apply Now! Are you passionate about deploying real-world machine learning solutions? We're looking for a versatile Data Scientist with deep expertise in Databricks, PySpark , and end-to-end ML deployment to drive impactful projects in the Retail and Automotive domains. What Youll Do Develop scalable ML models (Regression, Classification, Clustering) Deliver advanced use cases like CLV modeling , Predictive Maintenance , and Time Series Forecasting Design and automate ML workflows on Databricks using PySpark Build and deploy APIs to serve ML models (Flask, FastAPI, Django) Own model deployment and monitoring in production environments Work closely with Data Engineering and DevOps teams for CI/CD integration Optimize pipelines and model performance (code & infrastructure level) Must-Have Skills Strong hands-on with Databricks and PySpark Proven track record in ML model development & deployment (min. 2 production deployments) Solid grasp of Regression, Classification, Clustering & Time Series Proficiency in SQL , workflow automation, and ELT/ETL processes API development (Flask, FastAPI, Django) CI/CD, deployment automation, and ML pipeline optimization Familiarity with Medallion Architecture Domain Expertise Retail : CLV, Pricing, Demand Forecasting Automotive : Predictive Maintenance, Time Series Nice to Have MLflow, Docker, Kubernetes Cloud: Azure, AWS, or GCP If you're excited to build production-ready ML systems that create real business impact, we want to hear from you! Apply Now to chaity.mukherjee@celebaltech.com.

As Lead ML Engineer , you'll lead the development of predictive models for demand forecasting, customer segmentation, and retail optimization, from feature engineering through deployment. As Lead ML Engineer, you'll lead the development of predictive models for demand forecasting, customer segmentation, and retail optimization, from feature engineering through deployment. Responsibilities: Build and deploy models for forecasting and optimization Perform time-series analysis, classification, and regression Monitor model performance and integrate feedback loops Use AWS SageMaker, MLflow, and explainability tools (e.g., SHAP or LIME)

Hi everyone. Open Positions in the Cloud Engineer Role Greetings from Tekaccel! This is an excellent opportunity with us. If you have that unique and unlimited passion for building world-class enterprise software products that turn into actionable intelligence, then we have the right opportunity for you and your career. What are we looking for? Job Title: Cloud Engineer Location: Gurgaon (Hybrid)Minimum once a month onsite (may increase as per project needs like deployments) Experience Required: 4-6 years Hire type: Contract Job Description: We are seeking a cloud engineer with strong hands-on experience in operationalizing AI/ML workloads in a GCP cloud environment. The ideal candidate will be skilled in deploying, monitoring, and scaling ML models, with deep knowledge of Google Cloud’s ML services and CI/CD pipelines for ML systems. Key Responsibilities: Implement and operationalize AI/ML pipelines using GCP services Develop and manage CI/CD pipelines for ML workflows (Cloud Build, GitHub Actions, Tekton) Containerize ML workloads using Docker, deploy and orchestrate on GKE/Kubernetes Use Infrastructure as Code tools (Terraform or Deployment Manager) for provisioning Implement model deployment and monitoring frameworks Integrate DevOps practices into the ML lifecycle Collaborate with Data Scientists and Engineers to productionize ML models Skills Required: GCP Vertex AI, Data Fusion, Dataplex CI/CD for ML (Cloud Build, GitHub Actions, Tekton) Containerization: Docker, GKE/Kubernetes Infrastructure as Code: Terraform or Deployment Manager Experience with model deployment & monitoring Strong understanding of DevOps integration into ML lifecycle Medium to high hands-on expertise is mandatory Role Summary: You will be responsible for enabling, deploying, and managing ML workloads in production environments using GCP’s AI/ML stack. You will play a key role in scaling ML systems and ensuring robust, automated ML pipelines. If interested, candidates, please share your updated resume at naveen@tekaccel.com or WhatsApp at +91 7997763537 Tekaccel Software Services India

We are seeking an experienced ML/MLOps Engineer to join our team in India. The ideal candidate will have a strong background in machine learning operations, capable of bridging the gap between data science and production environments. Responsibilities Design, implement, and maintain machine learning systems and pipelines. Collaborate with data scientists to integrate models into production environments. Monitor and optimize the performance of deployed models. Automate and streamline ML workflows and processes. Ensure data integrity and quality throughout the lifecycle of machine learning projects. Develop CI/CD pipelines for ML model deployment and monitoring. Participate in code reviews and maintain documentation of ML workflows. Skills and Qualifications 4-10 years of experience in ML and MLOps roles. Strong programming skills in Python and familiarity with libraries such as TensorFlow, PyTorch, or Scikit-Learn. Experience with cloud platforms like AWS, Azure, or GCP. Proficiency in containerization technologies such as Docker and orchestration tools like Kubernetes. Knowledge of data engineering concepts and experience with ETL tools. Familiarity with version control systems, particularly Git. Experience with monitoring tools and frameworks for ML models.

We are seeking an experienced ML/MLOps Engineer to join our team in India. The ideal candidate will have a strong background in machine learning operations, capable of bridging the gap between data science and production environments. Responsibilities Design, implement, and maintain machine learning systems and pipelines. Collaborate with data scientists to integrate models into production environments. Monitor and optimize the performance of deployed models. Automate and streamline ML workflows and processes. Ensure data integrity and quality throughout the lifecycle of machine learning projects. Develop CI/CD pipelines for ML model deployment and monitoring. Participate in code reviews and maintain documentation of ML workflows. Skills and Qualifications 4-10 years of experience in ML and MLOps roles. Strong programming skills in Python and familiarity with libraries such as TensorFlow, PyTorch, or Scikit-Learn. Experience with cloud platforms like AWS, Azure, or GCP. Proficiency in containerization technologies such as Docker and orchestration tools like Kubernetes. Knowledge of data engineering concepts and experience with ETL tools. Familiarity with version control systems, particularly Git. Experience with monitoring tools and frameworks for ML models.