Top Machine Learning Jobs in Chennai

Design and build enterprise-scale AI search and retrieval systems using Azure AI Search and Azure OpenAI. Implement RAG/vector search, embeddings, prompt engineering, secure document-level access controls, scalable backend services, and observability to improve relevance, accuracy, and latency. Mentor engineers and drive architecture for secure, compliant enterprise RAG platforms.

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 optimize Angular front-end features that integrate AI/ML models and APIs. Collaborate with data scientists, backend engineers, and designers to implement responsive, secure UI components, visualize model outputs, and deploy AI-powered web functionality.

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.

Lead application systems analysis and programming for large-scale data and AI/ML solutions. Partner with management to define enhancements, ensure architectural compliance, develop coding/testing standards, resolve complex issues, coach mid-level developers, and drive operational/process improvements while maintaining regulatory and policy compliance.

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.

Design, train, optimize, and deploy production computer vision and visual search models for mobile and cloud. Build scalable training pipelines, model versioning, evaluation, and inference services. Improve robustness, mobile performance, and retrieval quality while collaborating with mobile, backend, and product teams to integrate and operationalize ML solutions.

Design and implement AI models, operationalize ML models, develop CI/CD pipelines for ML workflows, and provide technical guidance while ensuring data security compliance.

Senior technical leader defining evaluation-driven development, modeling strategy, MLOps and platform direction on AWS. Oversees multiple agile release cycles, ensures measurable business impact, mentors data scientists, sets governance for evaluation, synthetic data, validation, cost and compliance across the ML portfolio.

Lead strategy and development of AI/ML engineering solutions, enhancing decision-making and automation across the enterprise. Oversee advanced analytics and mentoring of data science teams.

Lead full ML lifecycle to design, build, and deploy AI/ML (including Agentic and LLM) solutions for enterprise-scale reconciliation. Analyze large financial datasets, define model roadmaps, integrate models into production, ensure scalability, manage risks, and communicate results to senior stakeholders.

Create and review realistic, locale-specific robotics and natural sciences tasks in Marathi; apply scoring rubrics; review and score AI and human responses; produce gold-standard solutions and expert feedback.

As an AI Data Expert, you'll evaluate AI-generated outputs, review text, analyze multimedia, and provide structured feedback based on cultural standards.

Create, adapt, and score domain-specific robotics tasks and rubrics in Telugu to evaluate AI systems; review submissions for accuracy, regulatory alignment, and produce gold-standard solutions and expert feedback.

Create and review realistic, domain-specific robotics tasks in Hindi; apply scoring rubrics; evaluate AI and human responses for accuracy, regulatory alignment, and professional quality; and produce gold-standard solutions and expert feedback.

Create and review realistic, locale-specific robotics and natural sciences tasks in Tamil to evaluate AI systems; apply scoring rubrics, review and score submissions for accuracy and regulatory alignment, and produce gold-standard solutions and expert feedback.

The Senior Manager, Machine Learning leads the Conversation Memory team, focusing on developing foundational AI features, ensuring operational excellence, mentoring engineers, and managing project timelines for ML solutions.

Develop and optimize machine learning models, manage project priorities, and improve real-world solutions using advanced ML techniques.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Lead design and production of computer vision biometric systems (face detection, quality, attributes, recognition). Build/train models, implement fairness benchmarking, own end-to-end ML pipelines on AWS, optimize for low-latency inference, and mentor engineers.

Lead end-to-end ML systems: data pipelines, model development (transformer-based detection), sim-to-real synthetic data, multi-sensor fusion, on-device real-time inference (Jetson), MLOps (experiment tracking, versioning), and production deployment and monitoring. Set ML roadmap and hire/lead as the team grows.