Top Machine Learning Jobs in Chennai

Lead strategy and execution for fraud risk management across the platform. Develop detection and prevention frameworks, optimize transaction screening, partner with Compliance, Data Science, Engineering and Product, oversee KPIs and vendor costs, lead a risk strategy team, and pilot AI/LLM-driven tools to enhance detection while enabling business growth and regulatory alignment.

The Senior Data Analytics Engineer at MariaDB designs and builds data pipelines, writes SQL queries, develops internal tools, and translates data into actionable insights. They will collaborate closely with stakeholders and leverage AI tools to enhance analytics workflows.

The Business Systems Analyst will collaborate with IT and business teams to enhance supply chain systems using Oracle Fusion Cloud technologies, delivering innovative solutions for various modules.

The role focuses on evaluating Generative AI solutions, ensuring quality standards, mentoring team members, and improving data models in an academic research setting.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

As an AI/ML Engineer, you will develop machine learning models, collaborate with teams, monitor performance, and improve processes for life sciences applications.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Develop, deploy, and scale end-to-end analytical solutions for the Vapor Phase Drying process. Build ML models and data pipelines, work with dashboards and model monitoring, use Python and ML libraries, leverage Azure data and ML services, apply time-series forecasting, collaborate cross-functionally, and ensure regulatory and internal compliance.

Research and develop domain-specific LLMs and generative models for materials science: pretraining, fine-tuning, alignment (e.g., RLHF), evaluation, dataset curation, and benchmark creation. Collaborate with scientists and engineers to apply generative AI to discovery and hardware design, publish research, and mentor junior team members.

The Staff Product Manager will lead product backlog for a Scrum team, collaborating closely with engineering and AI teams while driving customer outcomes and improvements through analytics and integration solutions.

The Operational Excellence Manager drives performance improvements, mentors site teams, implements lean principles, and integrates digitization strategies to achieve operational excellence.

Work on ML/DL and computer vision models for aerospace and defense (missiles, rocket telemetry, avionics). Train, test, and improve models; prepare and clean text/image/sensor datasets; build lightweight demo APIs with FastAPI/Flask; document experiments and collaborate with mentors on real-world avionics systems.

The Senior AI / ML Ops Engineer will design and operate Agentic AI systems, implement MLOps practices, and ensure production automation is secure and reliable.

Design, implement, and operate enterprise-grade ML infrastructure and MLOps pipelines. Build CI/CD for ML workflows, manage model lifecycle (registry, tracking, monitoring), automate orchestration and scaling, and ensure governance, security, and reliability. Collaborate with data science, engineering, and infrastructure teams and support production-ready AI/GenAI deployments across the enterprise.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

The role involves developing enterprise applications using Java and Spring, integrating AI features, mentoring junior developers, and collaborating with cross-functional teams.

As an ML Engineer, you will design, experiment, and deploy AI/ML models, evaluate their performance, and leverage Python and SQL.

Design, build, deploy, and maintain production-grade machine learning models and end-to-end pipelines. Perform feature engineering, model evaluation, optimization, REST API deployment, monitoring, and automated retraining. Collaborate with cross-functional teams to translate business requirements into scalable, reliable ML services using cloud, containerization, and CI/CD practices.

Design and implement scalable end-to-end AI/ML solutions and data pipelines; define secure cloud and on-prem architectures; lead MLOps, CI/CD, model lifecycle management, data governance, and cross-functional collaboration to drive AI initiatives in manufacturing.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Develop, train, evaluate, and deploy small task-specific ML models for real-time English conversational skills (ASR, pronunciation, grammar, recommendations). Create datasets and task-specific evaluations, improve internal SOTA, mentor engineers, and contribute to hiring.

As an AI Expert, you will review and evaluate AI-generated outputs, correct text, analyze multimedia content, and ensure cultural appropriateness and accuracy in the outputs.

Lead design, develop, test and support SAP ABAP solutions, deliver RICEFW objects, integrate SAP with third-party systems, optimize performance, produce technical documentation, mentor junior developers, and help adopt AI/automation in the SAP ecosystem.

Lead design, development, and deployment of production-grade LLM and RAG-based AI solutions. Improve retrieval performance, build automated evaluation and validation frameworks, instrument pgvector retrieval KPIs, integrate metrics into CI/CD, and mentor mid-level engineers while driving technical reviews and best practices.