Top Artificial Intelligence Jobs in Chennai

Lead Supabase’s AI-native operating system by defining maturity models, company-wide standards, assessment processes, operating cadences, and transparency mechanisms. Manage two senior contributors focused on platform and enablement, drive adoption without direct authority, replace unnecessary meetings with automated reporting, integrate standards into onboarding, and measure whether AI agents reduce organizational coordination costs.

Design and deliver production-grade AI/ML architectures for education and assessment platforms. Translate stakeholder requirements into secure, cost-aware blueprints covering RAG, multi-agent systems, LLM judging, OCR, human review, cloud infrastructure, LLMOps/MLOps, observability, governance, and evaluation. Lead workshops, architecture decisions, production readiness, FinOps, sovereign hosting, and technical mentorship across international government and education programs.

Architect and build production-grade agentic systems and shared AI platform services at enterprise scale. Develop agent orchestration, tool and MCP integrations, LLM gateways, evaluation frameworks, observability, safety controls, and cloud infrastructure. Drive reliability, cost optimization, AI-first engineering practices, technical standards, cross-team alignment, and mentorship while remaining hands-on with coding, architecture, and production debugging.

Lead the architecture and hands-on development of an AI-powered React application builder. Design scalable distributed systems, LLM pipelines, RAG and embeddings workflows, code-generation systems, publishing infrastructure, and AI guardrails. Improve observability, reliability, latency, safety, and fault tolerance while mentoring engineers and partnering with product, design, infrastructure, and security teams. This is an individual contributor role requiring production coding, system design leadership, and operational excellence.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

Leads the architecture, development, deployment, and scaling of enterprise AI solutions for finance automation. Integrates LLMs with SAP, Salesforce, and cloud platforms; establishes prompt engineering, CI/CD, security, governance, monitoring, and data practices. Mentors developers, leads technical strategy and vendor evaluations, presents to executives, and drives reliable AI platforms supporting financial processes, analytics, reporting, and global operations.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Build and lead the AI engineering platform for enterprise COBOL modernization. Responsibilities include designing transformer models for COBOL-to-Java/Python translation, developing high-performance code analysis engines, optimizing inference, architecting distributed cloud-native systems, and delivering reliable AI pipelines for large enterprise codebases. The role also includes hiring and leading engineering teams, shaping product strategy, and partnering with the CEO as a technical co-founder.

Develop reusable, accessible, and high-performing React and Next.js components for Sitecore XM Cloud and Sitecore AI. Build content models, rendering configurations, visual authoring experiences, and integrations using GraphQL, Experience Edge, and enterprise APIs. Implement AI-enabled search, recommendations, discovery, and personalization while optimizing performance, SEO, accessibility, testing, analytics, and security. Troubleshoot complex frontend and deployment issues, improve shared standards and tooling, and collaborate with architects, designers, content teams, marketers, and engineers.

Plans and executes manual and automated testing for Sitecore AI websites and web applications. Tests functionality, accessibility, performance, APIs, integrations, content workflows, personalization, and cross-browser behavior. Builds automation frameworks, integrates tests into CI/CD pipelines, documents defects, verifies fixes, tracks quality metrics, and collaborates with developers, designers, content teams, and product owners to improve release quality.

Build and deploy software products across frontend, backend, APIs, integrations, databases, and cloud technologies. Use AI-assisted development tools and agent frameworks to accelerate delivery while validating and improving generated code. Translate unclear requirements into testable steps, learn unfamiliar codebases, solve problems, and own features from requirements through production deployment.

Investigate escalated production incidents (DB anomalies, failed payments, data migrations), write post-mortems and runbooks, and build AI-powered automation (LLM diagnostics, agents) plus developer tooling. Maintain CI/CD, GCP infrastructure, container deployments, observability, and partner on ERP and payment provider integrations.

Design, build, deploy, integrate, and optimize production voice AI agents. Develop backend integrations with enterprise systems using APIs, WebSockets, webhooks, and CRM connections. Implement LLM orchestration, prompt design, guardrails, and tool calling. Monitor agent performance, analyze logs and traces, conduct root-cause investigations, and improve conversation funnels. Lead client workshops, collaborate with engineering teams, manage technical delivery, and support on-site deployment reviews.

Support quarterly forecasting and annual budgeting by consolidating business-unit submissions, preparing financial analysis and management reporting, modeling scenarios, identifying performance drivers, and improving FP&A automation and planning processes.

Design, develop, test, and maintain production software for semiconductor test systems. Integrate AI and LLM capabilities into Java- and C++-based platforms, develop Python and Shell automation tools, define system interfaces, and evaluate integrations for performance, reliability, security, and maintainability. Collaborate with architects and AI specialists, apply engineering best practices, lead integration projects, and provide technical guidance in Linux environments.

Develop and deploy traditional ML, Generative AI, RAG, and agentic applications. Responsibilities include designing AI systems, building APIs and end-to-end ML pipelines, leveraging AWS and Azure services, optimizing retrieval performance, implementing evaluation and observability, and supporting production deployments. The role collaborates with technical and business stakeholders across experiments, pilots, and scalable solutions.

Leads a digital consultancy practice by partnering with sales, developing client relationships, overseeing strategic technology programs, and expanding innovative offerings. The Director provides thought leadership, manages engagement profitability and delivery quality, develops business cases and technical roadmaps, and mentors consultants, project managers, and architects. Responsibilities also include recruiting, performance management, client presentations, proposal development, and guiding complex enterprise solution implementations.

Lead the architecture, development, deployment, and operation of AI/ML solutions for identity and access security. Build scalable pipelines, inference services, monitoring, and MLOps capabilities using cloud platforms. Integrate anomaly detection and behavioral analytics into IAM, PAM, and secrets-management workflows. Partner with cybersecurity and engineering teams, establish applied AI security standards, influence technical roadmaps, and mentor junior engineers.

Build and productionize LLM-powered agents, workflows, and RAG systems. Apply prompt engineering, embeddings, fine-tuning, and traditional ML to automate tasks and improve products. Mentor teammates and help design scalable, production-grade AI infrastructure while staying current on LLM advances.

Leads enterprise technology strategy, delivery, and transformation across Finance, FinTech, Legal, and Risk. Oversees product platforms, Agile and DevOps adoption, AI and data initiatives, digital transformation, change management, vendor partnerships, governance, and technology operations. Partners with executive stakeholders to align global priorities, integrate platforms, manage dependencies, and deliver scalable, secure, value-driven outcomes across a federated enterprise technology organization.

Designs, develops, deploys, and integrates AI, machine learning, deep learning, and generative AI solutions for industrial and business applications. Owns the machine learning lifecycle, solution architecture, data strategy, model monitoring, and production integration. Evaluates emerging technologies, manages technical delivery from partners and contractors, and collaborates with cross-functional teams to deliver scalable, reliable, and maintainable AI solutions.

Design and deliver production-ready AI, machine learning, and generative AI solutions for industrial and business challenges. Own the ML lifecycle, architecture, data strategy, deployment, monitoring, and integration across cloud and on-premises environments. Evaluate emerging technologies, guide internal and external partners, define technical requirements, and support adoption through documentation, training, and cross-functional collaboration.

Designs, develops, deploys, and integrates AI, machine learning, generative AI, and analytics solutions for industrial and business challenges. Owns the machine learning lifecycle, solution architecture, data strategy, production integration, monitoring, and technical evaluation of internal and external partners. Collaborates with cross-functional stakeholders, supports adoption, evaluates emerging AI technologies, and develops solutions such as predictive maintenance, anomaly detection, forecasting, document analysis, and knowledge tools.

Designs, develops, deploys, and integrates AI, machine learning, deep learning, and generative AI solutions for industrial and business challenges. Responsibilities include selecting modeling approaches, managing the ML lifecycle, defining solution architectures, analyzing operational data, supporting production deployment and monitoring, managing technical partners, and ensuring data governance, reliability, and maintainability. The role also evaluates emerging technologies, collaborates across functions, and supports user adoption and continuous innovation.

Designs, develops, deploys, and integrates AI, machine learning, generative AI, and optimization solutions for industrial and business applications. Owns the machine learning lifecycle, solution architecture, data strategy, model monitoring, and production readiness. Evaluates technologies, manages technical delivery from vendors and contractors, and collaborates with cross-functional teams to translate operational needs into scalable AI tools. Supports adoption, innovation, governance, and continuous improvement across energy and industrial environments.