Top Artificial Intelligence Jobs in Chennai

Designs, builds, evaluates, and deploys production-grade autonomous and multi-agent AI systems for cybersecurity detection and response. Develops LLM orchestration, tool use, memory, planning, RAG pipelines, evaluation frameworks, and scalable agent infrastructure. Optimizes reliability, latency, accuracy, cost, observability, and failure recovery while partnering with security researchers and product teams.

Develop AI assurance testing strategies for generative AI, LLM, RAG, and agent-based systems. Design functional, non-functional, safety, reliability, and responsible AI evaluations; curate golden datasets; generate synthetic data; implement Python evaluation frameworks; establish observability and tracing with OpenTelemetry; integrate evaluations into CI/CD; report responsible AI metrics; and perform monitoring, diagnostics, root cause analysis, and regulatory validation.

Associate Product Manager responsible for understanding customer needs, defining product opportunities, creating requirements and roadmaps, partnering with Engineering and Design, running experiments, analyzing metrics, and improving lending product outcomes. The role collaborates with Risk, Operations, Data Science, Compliance, and external partners while independently owning product initiatives and contributing to long-term product strategy.

Perform contract drafting, review, redlining, and simulated negotiations while evaluating AI-generated legal responses. Identify contractual risks, assess legal accuracy using structured rubrics, and provide actionable feedback to improve AI systems for contract review. Collaborate with legal, product, engineering, and research teams on datasets, evaluation guidelines, and quality standards.

Design, develop, and deploy AI-powered automation for hardware performance verification. Build intelligent tools and agents for test generation, regression detection, failure analysis, and corrective recommendations. Integrate LLM assistants, create verification data pipelines, establish impact dashboards, and collaborate with verification engineers. Apply generative AI, reinforcement learning, and formal methods to improve coverage, reduce cycle times, and increase productivity across complex GPU, CPU, memory, and HPC verification workflows.

Build and own Supabase’s AI tooling, including agents, MCP servers, agent skills, dashboard assistants, and developer-facing AI capabilities. Establish eval-first development through benchmarks, instrumentation, quality KPIs, and regression gates. Design how documentation and product knowledge are exposed to agents, create reusable AI patterns, and collaborate across Docs, CLI, Frontend, SDK, and infrastructure teams. Improve reliability, testing, authentication, hosting, and developer experience in a fast-moving, globally distributed environment.

Produces professional, brand-ready corporate videos using generative AI from concept through final export. Responsibilities include model selection, prompt and reference engineering, asset preparation, storyboarding, cinematography, editing, sound, color, quality control, governance, and workflow development. The role diagnoses generation issues, maintains reusable asset libraries, ensures brand and legal compliance, coaches creative teams, and presents work to stakeholders. Expert Higgsfield experience and a strong AI-generated video portfolio are required.

Conduct manual and automated testing across user interfaces, APIs, databases, and backend systems. Create test cases and data, execute functional and nonfunctional test types, validate data with SQL, automate tests using Playwright, perform API testing, track defects, and support Agile planning, risk assessment, and release sign-off. Use AI tools to improve quality engineering efficiency.

Designs and leads Azure AI and TokenOps architectures across Asian markets. Responsibilities include customer workshops, pilots, agent and RAG architecture, model routing and evaluation, observability, FinOps unit economics, enterprise integration, security, Responsible AI, and regulatory compliance. The role coordinates customers, partners, Microsoft stakeholders, and regional delivery teams while creating reusable, locally adaptable architecture and delivery patterns.

Leads QA strategy and quality initiatives for AI products, architecting scalable automation frameworks and developing testing methodologies for RAG, hallucination, drift, and bias. Owns the AI quality charter and roadmap, influences architecture and cross-functional teams, coaches QA staff, and drives reliability and risk reduction. Bonus responsibilities include CI/CD, cloud platforms, MLOps monitoring, and AI workload performance testing.

Own the architecture and technical direction of Distru’s AI product suite. Build and operate production multi-agent systems, MCP integrations, contextual memory and unified data layers, evaluation frameworks, observability, reliability, and safe workflow execution. Establish engineering standards, prototype emerging agentic approaches, collaborate with customers and product teams, and influence roadmap and technical direction.

Design and evaluate rigorous hardware engineering problems for AI training projects. Develop reference solutions using calculations, simulations, Python scripts, engineering software, and experimental reasoning. Validate models against physical test data, create verification methodologies, investigate hardware failures, and document reproducible technical solutions. The role requires substantial hands-on hardware engineering experience, strong programming ability, and mandatory experience with AI Coding Agents.

Supports the design, development, testing, deployment, and maintenance of cloud-based software in a microservices environment. Works with APIs, telemetry, structured and unstructured data, distributed systems, and ML/AI algorithms. Assists with automation, code reviews, refactoring, documentation, CI/CD, testing, and software integration while collaborating with product teams and stakeholders.

Shape conversational AI behavior by writing prompts, creating and annotating dialogue datasets, automating content workflows, testing model versions, evaluating responses, and identifying whether issues stem from prompts, datasets, or models. Collaborate with ML engineers to improve output quality, analyze user conversations, and maintain standards for tone, style, explicitness, and quality. The role requires strong English writing, hands-on LLM experience, analytical ability, and comfort with adult content.

Design and develop responsive web applications and reusable UI components while building AI-powered experiences. Integrate LLM and AI services, develop chatbots and copilots, implement prompt engineering and RAG architectures, and optimize applications for scalability, security, performance, and usability. Collaborate with product, design, data science, and backend teams, contributing to testing, deployment, code reviews, and production support.

Designs, builds, and deploys production AI systems, including LLM pipelines, RAG applications, agentic workflows, data platforms, APIs, and user-facing interfaces. Responsibilities include evaluating models, creating guardrails and observability, processing large-scale behavioral data, deploying cloud-based services, troubleshooting production systems, and iterating based on user behavior. The role requires full-stack development, strong Python and ML fundamentals, product thinking, and effective remote collaboration.

Lead revenue growth for Execo’s AI data services business. Own annual bookings and revenue targets, define go-to-market strategy, lead complex enterprise deals, expand strategic accounts, develop technology partnerships, and build scalable sales motions. The role requires executive-level engagement with AI labs, model builders, and enterprise clients, along with disciplined forecasting, pipeline management, pricing input, and collaboration with delivery and marketing teams.

Lead the design, development, and deployment of AI/ML systems for pricing, promotion optimization, demand forecasting, and price elasticity modeling. Build time-series, causal inference, reinforcement learning, recommendation, and optimization models across B2C and B2B channels. Architect production ML pipelines, data workflows, APIs, monitoring, retraining, and drift detection using cloud and data warehouse platforms. Collaborate with engineering, product, marketing, and analytics teams to improve profitability, revenue, and pricing decisions.

Leads enterprise AI programs, client engagements, GTM strategies, and proprietary AI tool development. Oversees complex AI/ML delivery, multidisciplinary teams, governance, and client-facing solution architecture. Drives adoption of Generative AI, Agentic AI, RAG, LLMs, vector databases, and scalable data platforms across cloud and hybrid environments. Provides technical mentorship and applies AI solutions to Life Sciences and pharmaceutical use cases including commercial operations, clinical insights, market access, and omnichannel analytics.

Leads delivery of enterprise conversational AI platforms, including chatbots and voice bots. Oversees Agile execution, technical architecture, cloud-native engineering, CI/CD, Terraform, Responsible AI, and AI adoption. Partners with product, security, architecture, and business teams while managing risks and executive communication. Mentors engineers and technical leads, supports hiring and performance management, and ensures scalable, secure, compliant, and operationally excellent solutions.

Leads software application development projects from design through deployment, translating business requirements into technical solutions. Oversees developers, budgets, testing, documentation, implementation, and stakeholder relationships. Performs proof-of-concept work, resolves technical issues, researches emerging technologies, and ensures quality assurance and automated testing. Manages team objectives while supporting application upgrades, training, and operational excellence across client projects.

Own product marketing, content strategy, brand storytelling, positioning, and organic growth for an early-stage AI and life sciences company. Develop narratives, campaigns, launch materials, customer stories, visual content, and distribution strategies. Collaborate with founders, product, engineering, design, sales, and customer-facing teams to translate complex technology into compelling market-facing content and drive brand awareness, qualified inbound interest, and pipeline influence.

Leads technical planning, requirements gathering, estimation, architecture, development, testing, documentation, deployment, and application support for client projects. Mentors developers, provides technical leadership, collaborates with architects and designers, translates creative designs into functional and technical requirements, and communicates effectively with technical and non-technical stakeholders.

Design, train, fine-tune, deploy, and optimize production AI systems including LLMs, RAG pipelines, agentic workflows, and machine learning solutions. Build APIs and inference pipelines, evaluate quality and performance, monitor production systems, and translate research into scalable applications. Collaborate with engineering, product, leadership, and global research teams while working a required overnight India schedule aligned with U.S. hours.

Defines and implements AI-driven architectures for next-generation wireless networks. Builds and tunes AI, ML, LLM, and SLM models; designs end-to-end systems spanning core, RAN, transport, edge, and data centers; identifies technology gaps; evaluates emerging technologies; and guides architecture decisions. The role requires deep wireless and telecom architecture expertise, systems engineering skills, hands-on C, C++, and Python development, and technical leadership across complex programs.