29 Ml Pipelines Jobs

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.

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

Key Responsibilities: AI/ML Architecture & Development Architect and implement end-to-end AI/ML solutions using Gen AI and LLMs tailored to business use cases. Design, fine-tune, and deploy large language models such as OpenAI GPT-4o, Gemini, Claude, LLaMA 3.2, and Phi. Build and scale production-ready applications using frameworks like FastAPI, Django, or similar. Develop, maintain, and optimize MLOps pipelines for automated model training, deployment, monitoring, and governance. Generative AI & NLP Expertise Lead the development and integration of generative AI models for applications including summarization, Q&A, sentiment analysis, and translation. Apply NLP and deep learning to enhance performance and reliability of models for complex user interactions. Engage in prompt engineering, vector DB integration, and fine-tuning using Azure OpenAI Service or equivalent platforms on AWS/GCP. System Design & Integration Collaborate closely with data engineering teams to build and manage scalable, cloud-native solutions. Design and deploy event-driven architectures and real-time data processing systems. Work with microservice architectures, containerization tools (Kubernetes, Docker), and cloud platforms (Azure/AWS/GCP). Frameworks & Tooling Use advanced frameworks like LangChain, LlamaIndex, Semantic Kernel, and AutoGen to build scalable LLM applications. Work extensively with data processing tools such as Apache Spark, TensorFlow, PyTorch, Pandas, and Scikit-learn. Technical Leadership Lead cross-functional AI teams and mentor junior engineers and data scientists. Establish and enforce development standards, best practices, and code quality benchmarks. Stay up-to-date with research and contribute to open-source initiatives where applicable. Project & Stakeholder Management Interface with business stakeholders to understand requirements, define AI/ML strategies, and deliver impactful solutions. Handle multiple engagements simultaneously while ensuring timely and high-quality project delivery. Support pre-sales and solutioning efforts including RFPs, RFIs, and client presentations. Required Qualifications: Proven expertise in designing and implementing LLM-based and Gen AI solutions. Hands-on experience with OpenAI, Claude, Gemini, LLaMA 3.2, Phi, and other foundational/open-source models. Strong command over NLP, deep learning, predictive analytics, and data modeling. Proficiency in cloud platforms (Azure preferred; AWS/GCP also acceptable), especially cloud-native and AI tools. Experience with distributed systems, APIs, event-driven architecture, and real-time data streaming. Solid experience with DevOps tools including Azure DevOps, MLflow, or other MLOps platforms. Strong understanding of user interaction patterns with large datasets and search systems. Knowledge of vector databases and semantic search (e.g., Pinecone, FAISS, Weaviate, etc.). Preferred Attributes: Demonstrated success leading AI projects from conception to deployment. Strong business acumen with ability to map AI solutions to business needs. Excellent problem-solving, communication, and collaboration skills. Experience working in Agile environments and managing multi-disciplinary teams.

As the Engineering manager, you will be managing the roadmap priorities for AI/MLSolution andwill bepoint of contactfor all technical issues and decisions, while managing stakeholders and partners across the organization. Provide technical leadership manage a high-performing engineering team to build and scale our next generationAI Solutions. Hire and directly lead a diverse team of top-tier ML and software engineers, taking charge of allfacetsof AI Products Design, Build,optimize, fine-tune Generative AI/LLM models/AI Solutions that can be integrated withnumerousapplications, support thousands of tenants, andoperateat scale in production to support variousPaypalAI Personalization initiatives Ensure high code quality, performance, and reliability through rigorous testing, code reviews, and adherence to software development best practices. Drive innovation by researching and incorporatingstate-of-the-artmachine learning techniques, tools, and frameworks into the platform. Effective communication, listening, interpersonal, influencing, and alignment driving skills; able to convey important messages in a clear and compelling manner Collaborate with cross-functional teams including product managers, data scientists, and software engineers and stakeholders to define platform requirements and priorities. Develop and evolve engineering processes and collaboration models tooptimizeteam efficiency and collaboration Mentor team members,providetechnical guidance, and foster a culture of collaboration, innovation, and continuous learning. Stay up to date with the latest advancements in AI/ML technology and industry trends andleveragethis knowledge to enhance the platforms capabilities What Do You NeedtoBring Qualifications Proven experience in leading or managing machine learning/AI teams, witha track recordof building successful AI solutions and productizing ML Models, Features Stores Solidtrack recordof over-achieving engineering and platform delivery and scaling targets in high volume, innovative and fast-paced high-pressure environment; proven results in delivery on platform products. Masters / bachelor s in computer science, Computer engineering, Machine Learning, Data Mining, Information Systems, or related disciplines, with technicalexpertisein one or more of the above-mentioned areas or equivalent practical experience. Experience developing Gen AI applications/services usingPrompt Engineering, LLMs, and fine-tuning methodsfor sophisticated business use caseswithlarge amountsof unstructured data. Strong background in deep learning techniques, particularly in NLP and Vision Proficiencyin multiple Programming/scripting languages,i.e.Python, Java, Scala,SQL, NoSQL (like HBase, Redis, Aerospike) Strongproficiencyin machine learning concepts, algorithms, and techniques, with hands-on experience in developing and deploying machine learning models. Expertisein buildingMLpipelines with Big Data technologies such as Hadoop, Spark, HBase, Kafka. Good understanding of distributed systems, data streaming, complex event Processing, NoSQL solutions for creating and managing data integration pipelines for batch and Real Time data needs. Experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, etc. Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes). Experience in Azure is a plus Strong communication, listening, interpersonal, influencing, and alignment driving skills; able to convey important messages in a clear and compelling manner Demonstrated leadership abilities, including the ability to inspire, mentor, and empower team members to achieve their full potential. Preferred Prior experience in Content Understanding, enrichment, entity resolution or knowledge graph Strong background in MLOps and experimentation frameworks Extensive experience with concurrent, parallel, and distributed computing, including performance tuning and optimization for large-scale applications

AI/ML Architect Location : Bangalore (Hybrid) Experience : 1015 years Key Requirements: Experience & Education 10+ years in total, 8+ years in AI/ML development 3+ years in AI/ML architecture Bachelor’s/Master’s in CS, AI/ML, Engineering, or similar Core Technical Skills Strong in TensorFlow, PyTorch Expertise in time series modeling and computer vision (object detection, facial/intrusion/anomaly detection) Hands-on with MLOps tools: MLflow, TFX, Kubeflow, SageMaker Experience in cloud (AWS, Azure, GCP) and edge AI (Jetson, Coral, OpenVINO) Proficient with Docker, Kubernetes, CI/CD pipelines Architecture & Integration Designed hybrid edge-cloud AI systems Integrated ML models into IIoT platforms Implemented model fusion with sensor, visual, and 3rd party data Leadership & Collaboration Mentored cross-functional teams (ML, Data, DevOps) Ensured security, compliance, and production readiness of AI models Translated AI strategy into business-aligned solutions

Job Description: Who are we Infosys NYSE INFY is a global leader in consulting technology and outsourcing solutions with annual revenues of over 15 64 B as of 2022 We enable clients in more than 50 countries to stay a step ahead of emerging business trends and outperform the competition Infosys Recognized as one of the 2022 World s Most Ethical Companies for the Second Consecutive Year by Ethisphere What are we looking for Lead Machine Learning Engineer We are looking for smart self driven high energy people with top notch communication skills intellectual curiosity and passion for technology in Machine Learning Space Our analysts have a blend of in depth domain expertise in one or more areas Retail CPG Logistics strong business and technical acumen along with excellent soft skills What do we require To work with clients to understand the issues they face diagnose problems design solutions and facilitate solution deployment on Azure ML One can be an individual contributor or lead small teams depending on the project You will be pivotal to understanding the requirement problem definition and discovery of the overall solution One will also have the opportunity to shape value adding consulting solutions for clients by connecting various functions of cloud components Industry knowledge Knows basics of machine learning is aware of cloud services Azure services has a deep understanding of coding practices knows how to guide teams on debugging the issues can connect the dots to arrivie at a solution and is very good at presentation of the ideas thoughts and solutions Key Responsibilities: Technical knowledge has expertise in cloud technologies specifically MS Azure and services with hands on coding to Expertise in Object Oriented Python Programming with 6 8 years experience 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 Domain Technical Tools Knowledge Object 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 Python programming with OOPs concept SQL XML YAML Bash JSON Pydantic models Class based frameworks Dependency injections FastAPI Flask Streamlit Python Azure API management API Gateways Traffic Manager Load Balancers Nginx Uvicorn Gunicorn Azure ML best practices in environment management run time configurations Azure ML Databricks clusters alerts Experience designing and implementing ML Systems pipelines MLOps practices and tools such a MLFlow Kubernetes etc 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 Architect Technical Requirements: Primary skills Technology OpenSystem Python OpenSystem 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: Good knowledge on software configuration management systems Strong business acumen strategy and cross industry thought leadership Awareness of latest technologies and Industry trends Education and Experience Overall 6 to 8 years of experience in Data driven software engineering with 3 5 years of experience designing building and deploying enterprise AI or ML applications with at least 2 years of experience implementing full lifecycle ML automation using MLOps scalable development to deployment of complex data science workflows Bachelors or Master s degree in Computer Science Engineering or equivalent Domain experience in Retail CPG and Logistics etc Azure Certified DP100 AZ AI900 Logical thinking and problem solving skills along with an ability to collaborate Two or three industry domain knowledge Understanding of the financial processes for various types of projects and the various pricing models available Client Interfacing skills Knowledge of SDLC and agile methodologies Project and Team management Preferred Skills: Technology->OpenSystem->Python - OpenSystem->Python

Responsibilities: Design and develop AI/ML models Process large dataset Build Python pipeline or React UIs deploy models as APIs or UI features Collaborate with teams, test models, present results Maintain clear documentation of tools and workflows

Role: Data Science Leader Experience: 10-15 years of overall experience, with at least 5+ years in Data science roles along with 24 years in a Leadership or Managerial capacity. Technical Skills: Data Science & Machine Learning Deep understanding of statistical modeling, predictive analytics, clustering, NLP, and time series forecasting. Strong grasp of model evaluation, fairness, explainability, and business alignment. Programming Proficient in Python (or R) with experience in data science libraries like pandas, NumPy, scikit-learn, TensorFlow, or PyTorch. Ability to read and review code, debug, and advise on best practices. Cloud Computing Working knowledge of at least one major cloud platform (AWS, GCP, Azure). Experience with cloud-native tools for data storage (S3, BigQuery), compute (EC2, GKE, Lambda), and ML services (SageMaker, Vertex AI, Azure ML). Data Engineering Fundamentals Understanding of data pipelines, ETL/ELT processes. Familiarity with tools like Airflow, dbt, Spark, SQL. Data Visualization Experience with dashboards and reporting tools (Tableau, Power BI, Looker, or custom visualizations using Plotly/Altair). Skilled in transforming complex outputs into clear, compelling narratives for non-technical stakeholders. MLOps / Deployment Familiarity with modern ML development and deployment practices. Basic understanding of deploying models to production, CI/CD pipelines, and monitoring. Generative AI Familiarity with GenAI concepts, including large language models (LLMs), embeddings, prompt engineering, and RAG pipelines. Familiarity with tools and APIs like OpenAI, Hugging Face, LangChain. Leadership & People Management Team Management Experience leading and mentoring teams of 510 individuals across varying levels. Proven track record of building and scaling Data science teams, delivering impactful projects Conduct performance reviews, manage career growth, and foster a healthy team culture. Cross-Functional Collaboration Proven ability to work closely with product, engineering, marketing, and business teams. Hiring & Talent Development Skilled in identifying top talent, conducting interviews, onboarding, and team capability building. Project & Stakeholder Management Project Management Experienced in managing multiple projects simultaneously. Comfortable with Agile methodologies, sprint planning, and delivery tracking. Stakeholder Communication Translating technical insights into business terms. Communicates technical insights clearly to senior executives. Problem Solving & Scope Management Ability to break down ambiguous business problems into solvable components. Define scope and ensure projects align with business impact. Strategic Thinking Aligns team objectives with company vision and business goals. Drives roadmap planning, and long-term capability building. Business Acumen Domain Knowledge Deep understanding of any one business vertical among the following e.g., telecom, BFSI, fintech, e-commerce, healthcare etc. Impact Orientation Focus on delivering measurable business value from data science efforts. Strong grasp of metrics, KPIs, and ROI-driven thinking. Strategic Thinking Ability to align data science efforts with long-term business goals. Soft Skills Exceptional communication, both verbal and written. Decision-Making: Balanced between data, intuition, team input and strategic vision. Empathy & Emotional Intelligence: Especially for managing team dynamics and motivation. Adaptability: In the face of changing priorities or business goals and organizational change.

This is an urgent and fast filling position - Need immediate joiners OR less than 1 month notice period AI/ML Engineer Location: Chennai Job Summary: We are looking for a Senior 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. Role & responsibilities 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.

Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment Collaborate with data scientists and software engineers to operationalize ML models Develop and maintain CI/CD pipelines for ML workflows Implement monitoring and logging solutions for ML models Optimize ML infrastructure for performance, scalability, and cost-efficiency Ensure compliance with data privacy and security regulations Strong programming skills in Python, with experience in ML frameworks Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes) Proficiency in cloud platform (AWS) and their ML-specific services Experience with MLOps tools Experience with ML model serving frameworks (TensorFlow Serving, TorchServe) Primary Skills Machine Learning CI/CD Pipelines Devops Secondary Skills Good Communication

Roles and Responsibilities: • Design, develop, and deploy machine learning models to solve real-world business problems. • Develop production-quality machine learning pipelines and frameworks using Python and Java. • Collaborate with data scientists, engineers, and business teams to define data requirements, model specifications, and system architecture. • Preprocess and clean large datasets, perform feature engineering, and ensure data quality for ML applications. • Implement, optimize, and deploy algorithms, ensuring scalability and performance in production systems. • Work with cloud-based platforms (AWS, GCP, Azure) for deploying and scaling machine learning models. • Stay current with the latest machine learning research and trends, and propose innovative solutions and techniques. • Document processes, models, and code to ensure reproducibility, maintainability, and knowledge sharing. Required Skills & Qualifications: • 5-6 years of professional experience in software engineering, with at least 2-3 years of hands-on experience in machine learning and data science. • Proficiency in Python for machine learning, data analysis, and algorithm development. • Fair experience with Java, including the development of production-level applications and integration with ML models. • Familiarity with data preprocessing techniques, including feature extraction, cleaning, normalization, and transformation. • Strong experience with SQL and working with large datasets from databases and data lakes. • Proficiency in cloud platforms (AWS, GCP, or Azure) for deploying machine learning models and managing infrastructure. • Strong knowledge of version control systems like Git and experience with CI/CD pipelines for machine learning models. • Strong problem-solving and debugging skills. • Ability to work independently and in a collaborative team environment. • Excellent communication skills for technical documentation and presenting solutions to stakeholders. Desired Skills: • Familiarity with big data technologies such as Spark, or Kafka. • Experience with DevOps practices for automating ML workflows. • Experience in deploying ML models using Docker, Kubernetes, or similar containerization technologies.

We are Hiring Senior Python with Machine Learning Engineer Level 3 for a US based IT Company based in Hyderabad. Candidates with minimum 7 Years of experience in python and machine learning can apply. Job Title : Senior Python with Machine Learning Engineer Level 3 Location : Hyderabad Experience : 7+ Years CTC : 28 LPA - 30 LPA Working shift : Day shift Job Description: We are seeking a highly skilled and experienced Python Developer with a strong background in Machine Learning (ML) to join our advanced analytics team. In this Level 3 role, you will be responsible for designing, building, and deploying robust ML pipelines and solutions across real-time, batch, event-driven, and edge computing environments. The ideal candidate will have extensive hands-on experience in developing and deploying ML workflows using AWS SageMaker , building scalable APIs, and integrating ML models into production systems. This role also requires a strong grasp of the complete ML lifecycle and DevOps practices specific to ML projects. Key Responsibilities: Develop and deploy end-to-end ML pipelines for real-time, batch, event-triggered, and edge environments using Python Utilize AWS SageMaker to build, train, deploy, and monitor ML models using SageMaker Pipelines, MLflow, and Feature Store Build and maintain RESTful APIs for ML model serving using FastAPI , Flask , or Django Work with popular ML frameworks and tools such as scikit-learn , PyTorch , XGBoost , LightGBM , and MLflow Ensure best practices across the ML lifecycle: data preprocessing, model training, validation, deployment, and monitoring Implement CI/CD pipelines tailored for ML workflows using tools like Bitbucket , Jenkins , Nexus , and AUTOSYS Design and maintain ETL workflows for ML pipelines using PySpark , Kafka , AWS EMR , and serverless architectures Collaborate with cross-functional teams to align ML solutions with business objectives and deliver impactful results Required Skills & Experience: 5+ years of hands-on experience with Python for scripting and ML workflow development 4+ years of experience with AWS SageMaker for deploying ML models and pipelines 3+ years of API development experience using FastAPI , Flask , or Django 3+ years of experience with ML tools such as scikit-learn , PyTorch , XGBoost , LightGBM , and MLflow Strong understanding of the complete ML lifecycle: from model development to production monitoring Experience implementing CI/CD for ML using Bitbucket , Jenkins , Nexus , and AUTOSYS Proficient in building ETL processes for ML workflows using PySpark , Kafka , and AWS EMR Nice to Have: Experience with H2O.ai for advanced machine learning capabilities Familiarity with containerization using Docker and orchestration using Kubernetes For further assistance contact/whatsapp : 9354909517 or write to hema@gist.org.in

We seek an innovative professional with expertise in AI tools like Copilot to boost engineering productivity. The role involves identifying bottlenecks, proposing tailored GenAI solutions, and integrating them into networking product development to streamline workflows and drive AI adoption across teams. Technologies: ------------------- GenAI Algorithms, LLM's, NLP, Hugging Face, RAG, OpenAI models, Microsoft and GitHub Copilot, Agentic AI, AI-powered tools, Advanced python for Datascience, Flash , REST API, debugging tools, parser tools, troubleshooting and investigating capabilities. Required Qualifications: ------------------------ 2+ years using GitHub Copilot, OpenAI, and other AI coding tools to boost development productivity. 2+ years in GenAImodel design, fine-tuning, and deployment. 3+ years in Pythonbuilding AI/ML pipelines, automation tools, and data processing scripts. Hands-on experience applying GenAI/ML for code generation, summarization, workflow automation, and virtual assistants. Delivered AI-driven impact in 23 engineering projects. Ability to identify and develop use cases in BI, automation, and areas like smart troubleshooting and adaptive testing. Proficient in LLMs, NLP, and AI/ML for software and networking applications. Knowledge of integrating AI with platforms like Webex, Zoom, and Teams via APIs. Skilled in scripting, automation, and AI-augmented workflows. Experienced in Agile/Scrum. Strong problem-solving, communication, and mentoring skills; able to work independently and collaboratively. Desired Skills: ------------------ GitHub Copilot certification. Experience with networking products software development. Familiarity with CI/CD, DevOps, and automation tools. Understanding of responsible AI practices. Experience with data analysis and visualization for AI productivity. Knowledge of cloud-based AI services and integrations. Primary Skills GenAI, LLMs, AI Tools, Python

We are seeking a highly skilled AI Engineer with demonstrated expertise in both traditional Machine Learning modelling and modern GenAI techniques. This role involves building scalable AI systems using foundation models, parameter- efficient fine-tuning (PEFT), and robust MLOps pipelines, while also grounding your solutions in classical machine learning principles. The ideal candidate excels at blending structured data modelling with large language model capabilities to drive intelligent, efficient, and production-ready AI systems. Key Responsibilities: Design, build, and deploy predictive systems by combining traditional ML models (e.g., regression, tree-based, time series) with Generative AI capabilities (e.g., LLMs, embeddings, GenAI-based simulation). Strong skills in prompt engineering are required to support the development and deployment of generative AI solutions. Fine-tune foundation models using PEFT techniques (e.g., LoRA, prefix tuning, adapters) for domain-specific applications. Build and maintain end-to-end MLOps pipelines for model versioning, testing, deployment, monitoring, and retraining. Use LLMs for automating feature generation, synthetic data creation, and prompt-driven scenario modelling. Manage the full AI/ML model lifecycle: experimentation, deployment, monitoring, diagnostics, and iterative enhancements. Apply and validate classical ML algorithms (e.g., XGBoost, LightGBM, ARIMA, SVMs, etc.) as benchmarks or hybrid components. Collaborate with cross-functional teams (data scientists, ML engineers, DevOps, product) to deliver AI solutions aligned with business needs. Deploy and monitor solutions in cloud environments (e.g., AWS SageMaker, GCP Vertex AI, Azure ML, REST(FAST API) with automated retraining and model health checks. Required Skills and Qualifications: Bachelors or Master’s degree in Computer Science, Machine Learning, Data Science, or related field. 3+ years of experience in AI/ML engineering, with proven success in both traditional predictive modelling and LLM-based development. Strong Python programming skills and familiarity with ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, and Transformers. Experience in parameter-efficient fine-tuning (PEFT) using frameworks such as Hugging Face PEFT, QLoRA, or AdapterHub. Proficiency in building MLOps workflows with tools like MLflow, Airflow, Kubeflow, or SageMaker Pipelines. Solid SQL/NoSQL experience and strong data wrangling skills. Hands-on experience deploying models in production cloud environments and containerized systems (e.g., Docker, Kubernetes). Excellent problem-solving, debugging, and communication skills.

Job Summary We are seeking an AI Leader for building Guardrail Platform to drive the design, deployment, and governance of AI guardrails that ensure ethical, responsible, and compliant AI operations . This role involves collaborating with cross-functional teams to implement AI fairness, explainability, bias mitigation, security, and regulatory compliance frameworks across AI/ML pipelines. Roles & Responsibilities AI Guardrail Strategy & Implementation Define and implement AI guardrails to ensure ethical AI development, risk mitigation, and compliance. Establish automated monitoring for AI fairness, bias detection, and explainability. Lead the operationalization of Responsible AI (RAI) principles across the organization. AI Risk & Compliance Management Align AI models with regulatory standards (e.g., GDPR, AI Act, CCPA, NIST AI RMF). Develop governance frameworks for model validation, auditing, and risk assessment . Collaborate with legal, compliance, and security teams to ensure AI transparency. AI Model Security & Reliability Implement guardrails against adversarial attacks, data poisoning, and model drift . Establish secure AI deployment standards to prevent unauthorized AI model access or misuse. Establish DevSecOps pipeline teams to integrate AI security best practices . Operationalization & AI Governance Define AI monitoring KPIs for continuous risk assessment and compliance tracking. Develop automated pipelines to flag high-risk AI behaviors and decision anomalies. Foster a culture of explainable AI (XAI) & transparency for AI-driven decision-making. Cross-functional Leadership & Innovation Partner with product, legal, and engineering teams to integrate AI guardrails into MLOps workflows . Stay ahead of AI regulatory trends, industry best practices, and emerging risks . Competencies Required Skills Education Skills (NOT TO BE USED)

Job Overview (Primary Skills - GCP, Kubeflow, Python, Vertex.ai) As a Machine Learning Engineer, you will oversee the entire lifecycle of machine learning models. Your role involves collaborating with cross-functional teams, including data scientists, data engineers, software engineers, and DevOps specialists, to bridge the gap between experimental model development and reliable production systems. You will be responsible for automating ML pipelines, optimizing model training and serving, ensuring model governance, and maintaining the stability of deployed systems. This position requires a blend of experience in software engineering, data engineering, and machine learning systems, along with a strong understanding of DevOps practices to enable faster experimentation, consistent performance, and scalable ML operations. What You Will Do Work with data science leadership and stakeholders to understand business objectives, map the scope of work, and support colleagues in achieving technical deliverables. Invest in strong relationships with colleagues and build a successful followership around a common goal. Build and optimize ML pipelines for feature engineering, model training, and inference. Develop low-latency, high-throughput model endpoints for distributed environments. Maintain cloud infrastructure for ML workloads, including GPUs/TPUs, across platforms like GCP, AWS, or Azure Troubleshoot, debug, and validate ML systems for performance and reliability. Write and maintain automated tests (unit and integration). Supports discussions with Data Engineers to work on data collection, storage, and retrieval processes. Collaborate with Data Governance to identify data issues and propose data cleansing or enhancement solutions. Drive continuous improvement efforts in enhancing performance and providing increased functionality, including developing processes for automation. Skills You Will Need Group Work Lead: Ability to lead portions of pod iteratives; can clearly communicate priorities and play an effective technical support role for colleagues. Communication: Maintaining timely communication with management and stakeholders on project progress, issues, and concerns. Developing effective communication plans tailored to diverse audiences. Consultive Mindset: Go beyond just providing analytics and actively engage stakeholders to understand their challenges and goals. Ability to have a business-first viewpoint when developing solutions. Cloud & ML Ops: Expertise in managing cloud-based ML infrastructures (GCP, AWS, or Azure), coupled with DevOps practices, ensures seamless model deployment, scalability, and system reliability. This includes containerization, CI/CD pipelines, and infrastructure-as-code tools. Proficiency in programming languages such as Python, SQL, and Java. Who You Are 5+ years of industry experience working with machine learning tools and technologies. Familiarity with agile development frameworks and collaboration tools (e.g., JIRA, Confluence). Experience using Tensorflow, PyTorch, scikit-learn, Kubeflow, pandas and numpy. and frameworks like Ray, Dask preferred. Expertise in data engineering, object-oriented programming, and familiarity with microservices and cloud technologies. An ongoing learner who seeks out emerging technology and can influence others to think innovatively. Gets energized by fast-paced environments and capable of supporting multiple projects - can identify primary and secondary objectives, prioritize time, and communicate timelines to team members. Dedicated to fulfilling ideals of diversity, inclusion, and respect that the client aspire to achieve every day in every way. Regularly required to sit, talk, hear; use hands/fingers to touch, handle, and feel. Occasionally required to move about the workplace and reach with hands and arms. Requires close vision.