Top Machine Learning Jobs in Chennai

Implement and deploy machine learning models, design data pipelines and integrations, build low-code AI-enabled legal workflow tools using tools like GitHub/Microsoft Copilot, ensure data governance and secure integrations, collaborate with Legal and tech stakeholders, and contribute to research, documentation, and continuous improvement.

Develop and optimize chemical processes for APIs, intermediates, and RSMs including literature review, lab feasibility, DoE, scale-up, technology transfer, impurity characterization, investigations, and compliance with cGMP and safety. Prepare protocols, reports, manage lab operations, equipment qualification, and lead/mentor lab staff. Preferred digital skills include Python, ML and AI.

Manage and grow a team responsible for observability, monitoring, and integrations across monetization systems. Improve reliability and data quality for billing, provisioning, subscriptions, and revenue-impacting workflows; define telemetry and integration strategies; implement anomaly detection and incident management; and partner with cross-functional stakeholders to ensure reliable customer and financial workflows.

Develop and deploy machine learning algorithms, evaluate their performance, improve ML systems, and collaborate with cross-functional teams.

Develops and deploys machine learning algorithms, evaluates their accuracy, creates data pipelines, and contributes to innovations and technical documentation. Guides less experienced colleagues and collaborates across teams.

The role involves designing and implementing machine learning systems, mentoring engineers, leading technical strategies, and ensuring system reliability and scalability.

Implement and evaluate machine learning models, manage model training and deployment, design data pipelines, build integrations and low-code solutions using AI-assisted tools, and ensure data governance and security while partnering with legal and technology stakeholders.

Manage cross-team delivery in an AI-native engineering organization, focusing on risk management, dependency resolution, and technical communication.

Design, develop, and deploy cloud-native chatbots and voice bots using Dialogflow CX and Google CCAI. Build LLM-powered playbooks, integrate backend APIs and databases, apply NLP/ML techniques (TTS/STT/SSML), troubleshoot distributed systems, and collaborate with cross-functional teams to deliver scalable conversational AI solutions.

Design, build and maintain scalable data-test automation frameworks using Python/PySpark and SQL on cloud (Azure/Databricks). Create and execute automated and manual test suites, write test plans, validate UI and services, integrate tests into CI/CD pipelines, collaborate with cross-functional Agile teams, and provide technical guidance and tooling for monitoring and deployment.

Design, prototype, and productionize computer vision and machine learning algorithms for semiconductor wafer inspection. Develop and optimize image processing for defect detection and classification, ensure real-time scalability and performance, and collaborate with cross-functional teams to integrate solutions across Windows and Linux environments.

Designs, builds, and deploys production-ready AI/ML applications and pipelines, integrating generative AI, deep learning, chatbots, and image-processing solutions on cloud or on-prem environments. Leads and mentors teams, coordinates cross-functional efforts, and drives technical decisions to meet project requirements.

Design, implement, and deploy production-ready AI/ML solutions using generative models, deep learning, and image/chatbot technologies. Lead technical decisions, mentor junior engineers, coordinate across teams, and manage cloud and on-prem AI pipelines to meet project requirements.

Lead the ethical use and governance of AI, ensuring compliance with legal standards, managing AI risks, and fostering collaboration across teams.

Design, develop, and implement advanced statistical and machine learning algorithms. Analyze disparate data sources to discover insights, build predictive models, prototype solutions, and collaborate with engineers to productionize models. Communicate findings to business and IT stakeholders and define system and functional requirements.

Design, build, and optimize generative AI applications including LLM features (chat, summarization, extraction), RAG workflows, embeddings and vector search. Improve model reliability and evaluation, implement integrations with AI APIs and vector databases, optimize performance (latency, throughput, compression), and collaborate across product, data, and engineering teams to deliver production AI features.

Lead and scale an AI/algorithm engineering team, define AI roadmap aligned to product goals, design and implement ML/DL/LLM solutions from research to production, integrate AI into products, drive adoption of advanced techniques (RAG, generative AI), collaborate with cross-functional teams, and mentor engineers to ensure scalable, production-ready systems.

Design, build and maintain scalable automation frameworks for ETL/big-data pipelines using Python/PySpark and Databricks. Perform manual and automated UI, API and service testing, develop CI/CD deployments, collaborate with cross-functional teams, and create monitoring utilities. Support test planning, debugging, and documentation for cloud-based data platforms.

The role involves developing AI/ML systems, specifically focusing on conversational AI and voice systems, using AWS services and creating backend solutions.

The Lead AI Engineer designs and scales AI-driven platforms for case management and customer service, collaborating with stakeholders to develop machine learning solutions and automate workflows.

Lead design, development, training, evaluation, and productionization of scalable ML/AI models using MLOps and CI/CD. Collaborate with data, product, and engineering teams, mentor engineers, enforce ML best practices, and ensure responsible, ethical AI and model monitoring in production.

Lead AI/ML engineering vision and roadmap for healthcare and life sciences. Oversee multiple client engagements, design and deploy scalable production-grade AI solutions (ML, NLP, deep learning, computer vision, LLMs), ensure regulatory readiness, responsible AI practices, and drive adoption and value realization while managing executive client relationships and expanding AI adoption.

ValGenesis is hiring software engineers at all levels, focusing on collaboration and innovation. Roles require 2 to 12 years of experience across various engineering disciplines.

Lead a team of ML engineers and data scientists, overseeing the design and deployment of machine learning models for life sciences applications while ensuring GxP compliance and technical governance.

The Senior Data Analyst will leverage data to inform strategic decisions, analyze complex datasets, develop predictive models, and communicate insights to stakeholders.