Top Machine Learning Jobs in Chennai

Analyze e-commerce, finance, growth, and operational performance to deliver actionable insights. Build forecasting and predictive models, evaluate projects and KPIs, support stakeholder decision-making, and foster a data-driven culture to improve business performance across departments.

Design, develop, prototype and deploy complex ML/DL and image-processing algorithms for semiconductor inspection systems. Build and train models using frameworks like TensorFlow or PyTorch, write model architectures from scratch, modify loss functions, and collaborate across teams to integrate ML solutions into products.

The AI Lead will design, develop, and implement AI solutions to enhance the SDLC stages, mentor teams, and ensure responsible AI practices.

As a Senior Impact Analyst, you will analyze sustainability challenges, design and manage interventions, and work closely with smallholder communities, emphasizing rigorous research and collaboration.

The Staff Engineer in Machine Learning will guide teams, solve client challenges, and enhance solutions leveraging their technical expertise.

Designs and operationalizes enterprise analytics, KPI frameworks, data validation pipelines, machine learning solutions, and governed GenAI capabilities. Builds advanced SQL and Python data transformations, RAG and agentic AI workflows, evaluation frameworks, and production-ready deliverables. Applies anomaly detection, forecasting, and other advanced analytics techniques while implementing testing, logging, guardrails, deployment packaging, documentation, and security controls.

As a Customer Support Engineer, you will resolve customer technical issues, perform installations, upgrade systems, and support field services while maintaining customer relationships.

Architect scalable, low-latency AI/ML systems and real-time inference pipelines for voice, vision, and NLP applications. Lead model productionization, infrastructure and framework selection, architecture reviews, optimization, observability, and MLOps best practices. Collaborate with product, data science, and engineering teams while mentoring engineers. The role requires expertise in distributed systems, LLM inference optimization, vector databases, RAG, containerization, cloud platforms, CI/CD, edge deployment, and software architecture.

Develop and deploy deep learning and reinforcement learning systems for urban air mobility, including routing and scheduling tools for aircraft networks. Build data and training pipelines, productize research prototypes, optimize inference performance, evaluate model behavior, and deploy AI tools to engineering teams. Responsibilities also include designing reliable production ML systems, constructing simulation environments, engineering rewards, and operating multi-step agentic workflows.

Designs and scales AI/ML platforms for pricing, promotion optimization, and demand forecasting across B2C and B2B channels. Develops time-series, causal inference, reinforcement learning, recommendation, and price elasticity models. Builds end-to-end ML pipelines, production API integrations, monitoring, retraining, and drift detection. Collaborates with data engineering, product, marketing analytics, cloud, and business teams to optimize revenue, profitability, promotions, competitive pricing, and customer-segment decisions.

Lead an 8-10 engineer team owning courier systems (dispatch, matching, pricing, ETA). Set technical direction, drive data-driven decisions and experiments, partner with data science, ensure reliability and scalability of low-latency distributed systems, and own hiring, performance, and operational excellence.

Lead post-deployment operations and optimization of Agentic AI, LLM integrations, and Python-based analytical pipelines. Supervise a data science operations team, ensure secure high-availability production systems, manage integrations with enterprise systems (SAP, Salesforce, Postgres), and drive continuous improvement, monitoring, and adoption of analytics solutions.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

Develop multi-agent automation platforms, enterprise LLM integrations, RAG pipelines, knowledge bases, and domain-specific fine-tuning solutions. Build computer vision systems for manufacturing quality control, process monitoring, and assembly-line digitalization. Optimize internal finance, HR, compliance, and operations workflows. Establish enterprise AI governance frameworks, including model evaluation and prompt management.

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.

Design, build, and deploy scalable ML models and pipelines from research to production. Own feature engineering, model tuning, real-time prediction, MLOps automation, monitoring, and lifecycle management to solve visibility, forecasting, and freight audit problems.

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.

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.

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.

Build and deploy production-grade generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, fine-tune and host LLMs, develop scalable FastAPI backends, optimize latency/cost, and containerize services for production.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, build, and scale production Generative AI systems: multi-agent workflows, advanced RAG pipelines, model fine-tuning and quantization, LLM integration and swapping, high-performance FastAPI backends, vector search and retrieval, containerized deployment, and monitoring for latency, cost, and reliability.

Design, build, deploy, and monitor ML models at scale. Collaborate with DS, QA, infra, and engineering teams to productionize and maintain ML services, optimize production code, and evolve solutions using modern ML practices.

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.

As a Demo Engineer, you support pre-sales by designing AI-driven demos and proof of concepts, collaborating closely with teams to showcase Cresta's capabilities.