Top Artificial Intelligence Jobs in Chennai

The Principal Applied AI Researcher will define and lead domain-specific model strategies, manage model lifecycle, mentor researchers, and influence platform decisions in AI research.

Lead enterprise clients to improve AI search visibility through analysis, custom GEO playbooks, C-suite presentations, prompt testing, monthly reporting, and cross-functional product collaboration to drive platform adoption and measurable visibility gains.

The role involves developing and implementing Machine Learning models, managing data pipelines, and supporting Data Science teams with best practices in ML operations and methodologies.

Lead the planning and execution of test strategies, develop automated test suites for UI and API, and mentor junior team members.

The Data & AI Operations Specialist leads technical operations for AI infrastructure, manages data pipelines, and oversees MLOps across multi-cloud environments, ensuring compliance and performance optimization.

The role seeks an AI Lead to drive Generative AI and Computer Vision projects, focusing on precise visual transformations and model training while mentoring engineering teams in best practices.

The AI Lead will design, develop, and implement AI solutions to enhance the SDLC stages, mentor teams, and ensure responsible AI practices.

The AI/ML Engineer II will design, develop, and deploy scalable AI/ML solutions, optimize model performance, and integrate AI into products. The role requires collaboration with various teams and knowledge in deploying solutions to cloud environments.

As Marketing AI and Automations Lead, you will manage automation architecture, integrate platforms, and develop AI-driven workflows to optimize marketing processes and strategies.

The role involves collaborating with Data Science teams to implement and manage ML models, designing GenAI solutions, and automating ML operations, while establishing best practices in ML lifecycle management.

The Technical AI Product Manager at CoinMarketCap will prototype AI solutions, identify user pain points, and collaborate with Engineering to validate and scale AI products in the crypto space.

The AI/ML Engineer II is responsible for designing, developing, and deploying AI/ML solutions. They work closely with product management and engineering teams to translate business problems into scalable AI systems, focusing on model optimization, cloud deployment, and cross-functional collaboration.

The AI Architect will lead technical execution of AI strategies, design autonomous agent systems, and guide clients in deploying Generative AI solutions.

The Product Analyst will identify growth opportunities, evaluate product changes through A/B testing, build dashboards, and communicate insights for data-driven decisions.

The role involves creating original math problems, auditing existing content for accuracy, operating AI tools for content generation, and collaborating with the academic team to deliver pedagogically sound math resources.

The Head of Product will lead the AIVM platform development, managing product strategy, execution, and cross-functional collaboration while focusing on AI and blockchain technologies.

Design and develop machine learning infrastructure and models, support product teams, build scalable data services, and promote collaboration within a diverse engineering team.

The AI Engineer will develop, deploy, and optimize AI solutions using Google's Gemini, working with machine learning models and data pipelines in a client-focused environment.

The AI Engineer will design and automate business processes using n8n, manage workflows, and collaborate with teams on technical requirements and solutions.

Lead customer-facing SaaS implementations and onboarding, design and deliver custom REST API integrations, troubleshoot complex technical issues, communicate clearly with stakeholders, and drive long-term customer success.

Build and deploy autonomous AI agents and internal copilots that research, enrich, reason, and execute GTM workflows. Implement RAG with embeddings and vector DBs, integrate agents with HubSpot and internal systems, automate data pipelines and scraping, and own agent systems end-to-end from prototype to production.