Top Artificial Intelligence Jobs in Chennai

Build the AI layer for SigNoz: implement agent orchestration, long-running execution, streaming APIs (SSE), and MCP server integrations to expose observability data to LLM agents. Ship production LLM features, design scalable distributed backends in Go and async Python, and drive projects end-to-end in a remote-first environment.

Execute and optimize SEO strategies across pages and channels using AI-assisted workflows. Perform keyword research, metadata and schema optimization, prompt refinement for AI outputs, monitor rankings and analytics (GA4, GSC, SEMrush), generate reports, QA junior deliverables, and collaborate with content and development teams to improve organic visibility and SEO processes.

The role involves managing business transformations and using supply chain analytics to enhance performance. Responsible for building advanced analytics models and providing strategic advisory services.

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.

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

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.

Design and operate large-scale GPU infrastructure for distributed AI training, ensuring reliability, performance, and efficient customer partnerships.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

The Site Reliability Engineer will provision and manage Kubernetes clusters, build automation tools, debug customer issues, and improve infrastructure reliability.

Own strategy, roadmap, and execution for HighLevel VoiceAI. Deliver reliable inbound/outbound AI voice agents integrated with telephony, CRM, workflows, and analytics. Improve agent setup, call quality, compliance, APIs/webhooks, monetization, and developer extensibility. Partner with engineering, design, support, and GTM to drive adoption, instrumentation, and measurable business outcomes for SMBs and agencies.

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.

Play modern AAA games to capture, record, and annotate structured gameplay data per instructions. Complete specific in-game tasks, troubleshoot recording issues, organize and securely transfer files, and report anomalies. Flexible freelance contractor role, fully remote.

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, build, and deploy end-to-end AI/ML solutions focused on LLMs and RAG. Lead data pipelines, vectorization, model fine-tuning, evaluation, and production LLMOps. Engage customers, translate business needs into technical requirements, and run workshops to enable adoption.

Lead full-stack development using Python and FastAPI; build high-performance APIs integrated with AWS (SQS, Lambda). Implement front-end using React/Angular, manage Mongo/MySQL/Oracle databases, integrate AI features via prompt engineering and GitHub Copilot/Cursor, optimize performance, apply DevOps/CI-CD, troubleshoot and collaborate in Agile/Scrum with Jira and Git.

Design, build, and deploy production-grade AI automation and agentic workflows for payments processes (reconciliations, audit and reporting). Integrate AI systems into operational pipelines to automate high-effort financial operations while preserving control, audibility, and reliability.

Design and lead GenAI and agent-based AI solutions for large-scale modernization programs. Define prompt engineering, orchestration, validation, guardrails, and human-in-the-loop workflows. Apply AI to legacy systems (COBOL, HP NonStop, C++) for code comprehension, dependency mapping, documentation, and test generation. Collaborate with solution and platform architects, work in Agile sprint-based delivery, and guide distributed offshore/onshore teams.

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.

As a Forward Deployed Engineer, drive customer success through AI deployments, optimize systems for reliability, and collaborate with teams to enhance products.

Design, build, and deploy production AI agent workflows and LLM-powered systems. Integrate tools, APIs, databases, and internal services; improve agent quality, latency, safety, and cost. Collaborate with product and operations to translate business problems into practical AI solutions and contribute to backend services and evaluation/guardrail patterns.

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