Top Artificial Intelligence Jobs in Chennai

The role involves leading full-stack development of the Agentic AI Platform, including backend and frontend implementation, technical design, and collaboration across teams.

As a DevOps Engineer II, you will enhance infrastructure management, automate CI/CD processes, and collaborate with development teams to optimize deployments and improve system reliability.

Lead strategy, roadmap, and outcomes for the messaging platform, virtual agent, multi-channel deployments, and AI services. Own agent productivity, AI-assisted features, channel coherence, documentation, customer engagement, prioritization, and metrics to drive measurable product outcomes.

The Product Manager will define and drive AI-powered statistics for life sciences, collaborate across teams, engage with customers, and support market differentiation strategies.

Lead end-to-end delivery of GenAI solutions: engage stakeholders, drive rapid prototyping, design enterprise-grade architectures (RAG, agents, HIL), mentor engineering teams, ensure security/privacy/governance, and take MVPs through UAT to production within compressed timelines.

Build and deploy AI-powered solutions including LLM bots, RAG agents, super-resolution CV models, and data pipelines. Integrate LLM APIs, design workflows, develop ML/DL models, set up GPU training, maintain ETL pipelines, and implement REST API and automation integrations. Collaborate cross-functionally and document models, schemas, and deployments.

Design, build, and operationalize scalable enterprise cloud services using Python (or Java/NodeJS/Ruby), FastAPI/Django, React/TypeScript, GraphQL, event-driven and streaming architectures. Lead CI/CD, Terraform modules, performance testing, release management, and safe LLM/GenAI integrations while advising management and enforcing responsible AI practices.

Design and review end-to-end ML architectures, build scalable models and MLOps pipelines, drive data and model lifecycle strategy, and create solution blueprints for backend and cloud-native systems while collaborating with product, data engineering, and infrastructure teams.

Develop ADAS computer vision, radar and sensor-fusion algorithms (stereo/mono camera, LiDAR, RADAR). Work on calibration, 3D reconstruction, SLAM, object tracking, filters, and production-grade deep learning models. Deploy and optimize CV/ML solutions on embedded/edge platforms using C/C++, Python, MATLAB, DL frameworks, and relevant toolchains.

Evaluate AI-generated technical content, craft and answer advanced ML/AI prompts, assess model responses for accuracy, relevance, and clarity, identify errors or hallucinations, and provide feedback to improve AI systems.

The job involves providing consulting services in data and AI, helping clients optimize their operations and drive digital transformation.

Combine creative copywriting with AI prompt engineering: design and optimize prompts for ChatGPT and other generators, improve output accuracy and tone, test prompts for marketing and automation use cases, collaborate with writers, designers and tech teams, document reusable templates and best practices, and track AI trends and tools.

Join a machine learning group to analyze data, research solutions, develop and prototype ML concepts, validate approaches, and specify software requirements for inspection tools; contribute to algorithm and product software development.

Design and deliver LLM-powered applications, build retrieval pipelines, and create evaluation frameworks. Collaborate with teams on innovative AI/ML solutions.

Lead and scale an AI/algorithm engineering team, define AI roadmap aligned to product goals, design and implement ML/DL/LLM solutions from research to production, integrate AI into products, drive adoption of advanced techniques (RAG, generative AI), collaborate with cross-functional teams, and mentor engineers to ensure scalable, production-ready systems.

Early-career full stack engineer role to build end-to-end product features, integrate AI coding tools into the workflow, mentor peers, improve cross-team efficiency, and contribute across the product lifecycle from design through deployment.

Lead and operate enterprise Azure and Microsoft 365 platforms, manage hybrid identity (Entra ID/AD), deliver and govern AI solutions (Azure OpenAI, Azure ML, Copilot), provide L2/3 support, secure and govern cloud resources, manage on‑prem infrastructure, and drive vendor, project, and stakeholder engagement to scale AI initiatives.

Develop and optimize AI models for efficient inference on custom silicon. Build fine-tuning pipelines, implement acceleration techniques (quantization, sparsity, distillation), create benchmarking and profiling tools, and collaborate with hardware, compiler, and quantization teams to translate algorithmic improvements into real hardware gains.

Review and validate neuroscience research, case studies, and AI-generated content; create clear explanations, annotations, and scenario-based exercises; identify errors and inconsistencies; apply current neuroscience methods and provide structured, evidence-based feedback to improve content quality for AI training.

Review biochemical datasets, protocols, and literature to evaluate AI-generated content for scientific accuracy. Develop realistic scenarios and training materials, identify errors or missing context, provide structured, evidence-based feedback, and explain complex biochemical concepts clearly. Participate in iterative content review and validation to improve AI understanding of life science topics.

Review and validate pharmacology content, create realistic cases and structured datasets, evaluate AI-generated pharmacology responses, identify errors and missing context, provide evidence-based feedback, and translate complex pharmacological concepts into clear training materials while documenting methodology and collaborating with multidisciplinary teams.

Design, build, test, and ship production AI-enabled software and reusable platform components. Integrate AI services, APIs, retrieval patterns, and business logic. Collaborate with U.S. partners, write tests, participate in code reviews and on-call rotation, debug production issues, and mentor junior engineers.

Design and implement the core AI layer for enterprise workflows: document extraction, retrieval, ranking, evaluation, agentic task execution, structured data generation, and integration into a polished product with emphasis on accuracy, traceability, and human review.

Design, build, and own AI-powered full-stack platforms and features from discovery through production. Embed AI into products and workflows, ensure reliability and security, support incidents, and mentor teammates.

Design polished, user-centric mobile and web interfaces for BJAK's AI-driven neobank products. Convert requirements into wireframes, flows, prototypes and high-fidelity UI. Maintain and evolve the design system, ensure accessibility and responsiveness, iterate using research and analytics, and collaborate with PMs and engineers to deliver pixel-perfect implementations across insurance, payments, investments and related financial services.