Top Artificial Intelligence Jobs in Chennai

Native German speaker will evaluate AI-generated email responses for accuracy, relevance, helpfulness, and personalization using their professional Gmail history; compare outputs, identify errors or unsupported personalization, provide structured feedback, follow guidelines, and maintain confidentiality over a 16-week contractor assignment.

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.

Design and deploy production AI systems that integrate backend services, build reusable ML infrastructure, optimize AI systems, and mentor engineers on best practices.

The Senior Director Analyst develops insights on data management platforms, leads the research agenda, engages clients, and mentors junior analysts, utilizing extensive expertise in data management and AI solutions.

The Partner AI Deployment Engineer will guide AWS engagements, shape technical strategy, build AI architectures, and mentor partners to drive successful AI deployments.

Lead global quality engineering and testing practices for AI-driven SaaS products, enhance automation, and ensure high-quality releases through effective strategies and collaboration.

The Principal Product Manager for Agentic AI Platform will lead the AI strategy and lifecycle, define capabilities, and ensure collaboration for scalable AI solutions in enterprise SaaS.

As a Senior AI Engineer, you'll design and build production AI systems for document processing and conversational AI on enterprise data, utilizing tools like Databricks and Azure.

Lead product quality across the SDLC by reducing defects, driving root-cause analysis, defining automation strategy and quality gates, building automated test frameworks (Playwright, API, integration, performance), integrating tests into CI/CD, enforcing release readiness, and using Claude Code and other AI tools to analyze issues and improve engineering outcomes.

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.

Evaluate AI-generated responses for accuracy, relevance, helpfulness, and personalization using a primary Google Workspace business Gmail account. Compare outputs, identify incorrect or unsupported personalization, provide structured feedback, follow project guidelines, maintain confidentiality, and complete evaluations within timelines.

As an AI Content Manager, you'll oversee content production, design workflows, ensure quality across languages, and leverage AI to enhance content output.

Design and build reproducible, containerized cloud platform challenges covering networking, IAM, observability, deployments, scalability, and disaster recovery. Develop automated validation tests, reference and defective solutions, debug environments, and document architecture and validation logic.

Join as an early-career full stack engineer to build frontend and backend product features, integrate AI coding tools into development workflows, translate product requirements into implementations, and grow toward autonomous full stack delivery while improving cross-team processes.

Early-career full stack internship developing frontend and backend features under senior guidance. Integrate AI coding tools into workflows, translate product requirements to implementations, contribute across the product lifecycle, and suggest cross-team process improvements while learning full-stack practices.

Design and lead enterprise-scale Agentic and Generative AI architectures, multi-agent systems, and knowledge-driven retrieval solutions. Define governance, observability, LLM selection, and production best practices. Lead PoCs to production, mentor teams, and advise stakeholders on AI strategy and transformation.

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.