Top Artificial Intelligence Jobs in Chennai

Design, develop, and deploy Gen AI solutions (chatbots, IVR, banking) using LLMs, Langchain/Haystack, vector/graph DBs, and speech-to-text. Build semantic search, embeddings, and scalable systems; collaborate cross-functionally and document work.

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, fine-tune, and deploy generative AI models (LLMs, diffusion, VAEs). Build data pipelines and APIs, integrate AI into production, optimize model performance and latency, and collaborate with cross-functional teams to deliver AI-powered solutions.

Develop and maintain AI-assisted development capabilities for the Eclipse-based SmarTest semiconductor testing environment. Integrate and optimize GitHub Copilot, resolve Eclipse and integration defects, implement customer-specific features, and perform testing and quality assurance. Translate customer requirements into technical solutions, provide technical assistance, evaluate changes in AI and Eclipse technologies, and collaborate with Microsoft, Advantest teams, and external partners to improve developer productivity and user experience.

Own enterprise AI engagements from discovery through production deployment. Design, build, integrate, debug, evaluate, and harden agentic systems using LLMs, RAG, tool calling, workflows, and guardrails. Lead technical workshops, proofs of concept, solution scoping, and executive ROI discussions. Measure adoption, containment, accuracy, and cost outcomes; create reusable delivery assets; enable partners; and provide product feedback. The role is customer-embedded and requires sustained on-site presence during engagements.

Design and deliver intelligent platforms, automation solutions, and AI-driven capabilities. Analyze complex production systems, identify failure modes, and improve reliability, efficiency, productivity, and mean time to recovery through AI, automation, operational intelligence, and modern engineering practices. Build scalable cloud-native solutions using Kubernetes, APIs, MLOps, generative AI, and workflow orchestration while mentoring teammates and driving continuous improvement.

Design and manage AI architectures using AWS, develop cloud-native solutions, mentor teams, ensure compliance, and evaluate emerging technologies.

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.

As an AI Intern at Valeo, you'll support innovative projects aimed at developing environmentally friendly mobility solutions while working in a diverse, global team.

Lead implementation of AI and automation across legal functions: deploy contract intelligence, fraud detection, and KYC automation; digitize manual legal workflows and inbox triage; build self-service document tools; maintain the legal tech stack and KPIs; train legal teams to drive adoption. First-six-month goals include KYC digitization, AI-driven inbox triage, and a contract analysis tool that reduces review time by 30%+.

Lead design and delivery of AI-driven applications and analytics for engineering and manufacturing. Research AI technologies, build PLM-based automation, collaborate with global teams, deploy IT infrastructure, and maintain academic partnerships to accelerate digital transformation.

Integrate and deploy AI-driven HVAC control solutions by mapping client data to internal standards, testing and deploying algorithms, supporting project handoffs, documenting building HVAC systems and control points, updating configurations, and coordinating cross-functional teams to ensure timely, standardized digitalization and post-deployment support.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Build and operate backend services and infrastructure for LLM- and voice-driven healthcare applications. Implement APIs, productionize LLM workflows, deploy cloud containerized services, and monitor performance and reliability end-to-end.

Evaluate and annotate physics content, create problem sets and technical documentation, and assess AI-generated responses for scientific accuracy. Contribute to dataset creation and provide expert feedback to improve AI model training. Collaborate remotely with project teams to ensure timely, high-quality deliverables and improve evaluation methodologies.

Contractor engineering SME to review and create technical engineering content for AI training: analyze documentation, develop examples and datasets, evaluate AI outputs for technical accuracy, and collaborate with teams to improve model performance and processes.

Contractor role to create original, high-quality financial documents (reports, analyses, models) with rich visuals (tables, charts, graphs) to train AI. Ensure accuracy, anonymize sensitive data, contribute long-form documents (10–100 pages), and clarify submissions as needed.

Remote contractor role to create and evaluate mathematical problems, proofs, and datasets to train and improve AI models. Assess AI-generated solutions for correctness, produce technical documentation, and collaborate with cross-functional teams to develop benchmark datasets and provide actionable feedback to improve model reasoning.

Build and maintain Python backend services and internal tools for AI/LLM applications. Integrate LLM APIs, design scalable REST APIs and async workflows, handle prompt management and model routing, monitor performance, debug distributed systems, automate data workflows, and collaborate to productionize AI features with emphasis on scalability, reliability, and safety.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Implement and customize ServiceNow ITSM and Service Portal solutions, perform integrations (REST, SOAP, MID Server), maintain Discovery and CMDB, develop and optimize server/client scripts (Business Rules, Script Includes, UI Policies), run workshops/demos, translate client requirements into user stories, mentor junior developers, and participate in Agile delivery cycles.

Lead design, customize, and implement ServiceNow ITSM, Service Portal, and ITOM solutions. Perform integrations (REST, SOAP, MID Server), Discovery and CMDB maintenance, and develop/optimize scripts. Run workshops, mentor junior developers, and participate in Agile delivery to translate client requirements into scalable ServiceNow solutions.

Develop, fine-tune, deploy, and optimize AI/ML models and LLM-based RAG systems using Python, AWS Bedrock, and major ML frameworks. Build scalable REST APIs and microservices with FastAPI or Flask, deploy containerized solutions on AWS, and manage CI/CD. Design databases and automated ETL pipelines, integrate cloud services, and document AI workflows while collaborating with cross-functional technical and product teams.

Lead design and implementation of agentic AI enablement systems: build end-to-end agentic workflows (orchestration, guardrails, evaluation), maintain retrieval/search infrastructure (indexing, ranking, relevance), integrate MCP/APIs, and create human-in-the-loop editorial and enablement tooling with cross-functional stakeholders.

Own product areas in BJAK's AI Neobank app (insurance, payments, savings, investing, travel). Define problems, scope, success metrics and roadmaps; collaborate with engineering, design, data, ops, compliance and business teams; launch features, monitor performance, run experiments and iterate to improve user experience and business impact.