Top Artificial Intelligence Jobs in Chennai

Hands-on pre-sales technical lead who designs AI/data solution architectures, builds POCs and reusable assets, authors RFP responses, and transitions solutions to delivery across cloud data platforms, ETL/ELT, and applied ML to drive client outcomes.

Lead design and architecture of Generative AI solutions on Azure and OpenAI, build end-to-end data pipelines and cloud infrastructure, produce solution blueprints, collaborate with stakeholders, and implement data preprocessing, API deployment, monitoring, and analytics delivery.

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, 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.

Lead pre-sales and technical delivery for Microsoft Industry Solutions, mapping customer needs to Microsoft Cloud and AI solutions. Drive project planning, risk mitigation, stakeholder engagement, IP reuse, and business development while mentoring teams and ensuring operational compliance.

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.

Lead a hands-on AI-first engineering pod building production software for a regulated payments platform. Design agentic orchestration pipelines integrated with Slack, Jira or Linear, Git, pull requests, and CI/CD. Ship product prototypes, improve .NET systems, establish secure engineering guardrails, and promote responsible AI-assisted development. Lead modernization of a legacy .NET Core 2.1 estate, mentor distributed engineers, grow the pod into scrum teams, and influence technical and business stakeholders with minimal direction.

The AI Engineer will develop and deploy GenAI applications, serve as a technical advisor, and collaborate on strategic initiatives in AI at Databricks.

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 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 maintain scalable Java and Spring Boot backend services, REST APIs, microservices, and event-driven systems. Build cloud-native applications using Docker, Kubernetes, and AWS while ensuring reliability, security, observability, and performance. Use AI-assisted development tools for coding, testing, debugging, documentation, and reviews. Participate in architecture discussions, production support, incident response, and on-call rotations. Collaborate across engineering and product teams and mentor other engineers.

Lead the architecture and implementation of a production-grade generative AI media platform. Build reproducible, versioned pipelines, train LoRAs, integrate control techniques (ControlNet, IP-Adapter, inpainting), automate QC against product references, deploy internal tools, and partner with design to own the technical roadmap and enable creative teams.

Develop, deploy, and scale AI/ML and generative AI solutions for a fintech platform. Design data models and pipelines, build agentic frameworks, integrate OCR and vector DBs, implement RAG, and champion MLOps and cloud deployment practices while mentoring teammates.

The role focuses on designing AI-driven workflows, developing user-friendly interfaces for complex backend logic, and enhancing UI/UX with real-time interactions and performance optimizations.

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

Lead global sourcing and vendor management for HPC and sovereign AI infrastructure. Drive contract negotiations, mitigate semiconductor supply risks, align procurement roadmaps with engineering, and ensure delivery of integrated components (GPUs, NVIDIA Grace, NVLink, InfiniBand, ConnectX-8) while meeting compliance and sovereignty requirements.

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.

The Forward Deployed Senior Software Engineer will lead initiatives in building custom solutions and algorithms for healthcare data, collaborating with various teams to enhance customer experience and project delivery.