Top Artificial Intelligence Jobs in Chennai

Architect and optimize production AI and Snowflake infrastructure, including scalable warehouses, Snowpark workloads, secure data and feature pipelines, model enablement, automation, CI/CD, observability, governance, and cost management. Evaluate infrastructure and technology options, establish architecture standards and roadmaps, integrate AI/ML systems into enterprise environments, lead technical workstreams, mentor engineers, and ensure performance, security, compliance, reliability, and scalability.

Researches funding opportunities and writes, reviews, and manages grant applications for nonprofit partners. The role requires translating complex program information into persuasive funder narratives, using AI tools thoughtfully, maintaining accuracy and deadlines, managing relationships with 25–30 partners, and improving scalable grant-support processes.

Lead enterprise AI architecture across generative AI, LLM applications, agentic systems, data, models, inference, security, governance, observability and FinOps. Define Azure AI target architectures, platform strategies, technology selections and transformation roadmaps. Guide domain architects and senior engineers while advising executives on AI investments, regulatory controls, responsible AI, enterprise integration, operational readiness and measurable business outcomes.

Design, build, automate, monitor, and optimize scalable AI/ML infrastructure on Microsoft Azure. Configure GPU-accelerated compute, Kubernetes, storage, networking, security, CI/CD, model deployment, observability, and MLOps systems. Develop infrastructure-as-code and automation, troubleshoot production workloads, optimize cost and performance, and collaborate with data scientists, ML engineers, and architects to integrate AI models into enterprise platforms.

Architects and engineers large-scale Databricks AI/ML infrastructure, including compute clusters, distributed training, data and ML pipelines, model serving, governance, CI/CD, observability, and cost optimization. Evaluates architecture alternatives, leads assessments, defines roadmaps and capacity plans, ensures security and compliance, and mentors engineers. Requires strong experience with Databricks, Python, SQL, Spark, Terraform, MLOps, cloud platforms, and production AI systems.

Designs, builds, integrates, tests, and operationalizes enterprise-grade LLM, GenAI, and agentic AI solutions on Azure. Responsibilities include developing RAG pipelines, agents, orchestration workflows, data ingestion and retrieval systems, safety controls, secure integrations, observability, evaluation frameworks, and production deployment patterns. The role provides hands-on technical leadership, collaborates with architects and security stakeholders, and creates maintainable engineering documentation and reusable components.

Designs, builds, integrates, tests, and operationalizes enterprise LLM, generative AI, agentic AI, and RAG solutions on Azure. Responsibilities include developing agents, retrieval pipelines, tool integrations, safety controls, observability, governance, and scalable production components. The role requires hands-on Python engineering, secure API and cloud integration, collaboration with architects and data teams, and technical leadership across client engagements.

Design, build, deploy, and govern production AI and GenAI solutions using Azure. Develop RAG, NLP, document intelligence, classification, and prediction applications; implement evaluation, explainability, safety, secure APIs, MLOps, compliance, and audit controls. Collaborate with data and governance teams, prepare AI-ready datasets, document solutions, and coach client counterparts on sustainable AI engineering practices.

Leads the end-to-end architecture, development, deployment, and operation of enterprise AI, machine learning, and GenAI solutions. Responsibilities include RAG, NLP, responsible AI, secure APIs, MLOps/LLMOps, evaluation, monitoring, compliance, and governed data integration. The role mentors engineers, leads technical reviews, advises stakeholders, and establishes engineering and responsible-AI standards for production deployments in regulated government environments.

Leads enterprise Data and AI strategy, P&L, consulting sales, delivery, and regional growth across MENA, the UK, and Europe. Oversees cloud data platforms, AI/ML lifecycle management, DataOps, governance, responsible AI, and client solutions. Builds delivery centers and high-performing teams while mentoring senior leaders, guiding architecture, developing partnerships, supporting RFPs, and translating business needs into scalable data and AI products.

Develop and modernize large-scale healthcare applications using Angular, Java, Spring Boot, SQL, and PowerBuilder. The role involves leveraging AI-assisted development tools, building secure and scalable solutions, optimizing databases, troubleshooting complex issues, analyzing requirements, and collaborating with internal and external stakeholders.

Lead and manage application development teams while designing, developing, and deploying scalable Generative AI solutions. Build Python backend services, integrate GenAI APIs, implement RAG systems, and ensure application performance, security, testing, and reliability. Provide technical oversight, review solutions, allocate resources, manage budgets, oversee staff, assess risks, and collaborate with product and engineering stakeholders.

Architects and deploys production-grade generative AI, agentic workflows, advanced NLP, RAG pipelines, and fine-tuned language models. Establishes LLMOps standards, guardrails, evaluation, optimization, and governance; advises clients and executives on AI strategy and business impact. Leads proposals, technical evangelism, reusable accelerators, code reviews, and mentorship while delivering secure, scalable enterprise AI systems.

Lead applications development teams while designing, developing, and deploying scalable Generative AI solutions. Build Python backend services, integrate GenAI APIs, implement RAG systems, optimize application security and performance, and ensure testing and code quality. Responsibilities also include technical oversight, hiring and personnel management, resource allocation, risk and compliance management, budget participation, and cross-functional collaboration.

Leads product strategy, roadmap, prioritization, technical scope, and launch planning for an AI-native communication application. Partners with engineering, design, and business stakeholders on architecture, scalability, reliability, and product trade-offs. Defines success metrics and uses adoption, engagement, retention, and user feedback to guide investments. Requires extensive technical product management experience and a track record of launching and scaling complex products.

Own the reference architecture and platform for production AI agents, including agent execution, tool use, state management, recovery, context engineering, model routing, guardrails, and human handoffs. Build shared retrieval, memory, and evaluation infrastructure for offline and online quality measurement. Integrate agents with event-driven systems and customer APIs, review designs, mentor engineers, and connect technical metrics to supply chain business outcomes.

Drive adoption and value realization of Neara’s digital-twin platform among Japanese utility customers. Responsibilities include customer engagement, platform enablement, training, ROI and business-case development, stakeholder management, solution expansion, and collaboration with Sales, Deployment, Product, and Engineering teams. The role requires translating customer strategic goals into technical solutions and measurable business outcomes, with regular customer and team travel in Japan and internationally.

Leads the commercial application of AI and data science across Verizon’s Home and Mobility businesses. Oversees propensity modeling, microsegmentation, customer lifecycle optimization, ROI measurement, model adoption, and production integration. Acts as a strategic bridge between technical AI teams and business leaders, translating analytical insights into revenue growth, retention, and customer lifetime value improvements. Mentors data scientists and partners with engineering, MLOps, IT, marketing, product, sales, and customer experience teams.

Leads the design and development of generative and agentic AI applications using LLMs, NLP, deep learning, retrieval, RAG, and orchestration frameworks. Builds agent architectures, evaluation frameworks, data-processing pipelines, guardrails, and responsible AI controls. Optimizes models through prompting, reranking, embeddings, model selection, and fine-tuning. Translates business needs into measurable AI solutions, stays current with emerging technologies, leads technical discussions, and mentors team members.

Leads the architecture, development, and production delivery of AI/ML capabilities in enterprise healthcare applications. Builds ML and generative AI solutions, RAG systems, model-serving APIs, MLOps workflows, distributed cloud services, and CI/CD pipelines. Ensures responsible AI, security, privacy, reliability, monitoring, and operational readiness. Influences architecture across teams, mentors engineers, documents technical decisions, and delivers reusable production components.

Lead Samba’s India sales presence from Mumbai, owning strategic deals while building and managing a growing sales team. Develop the regional go-to-market strategy, drive revenue across agencies and brand advertisers, advise partners on data and analytics solutions, represent the company at industry events, and collaborate with global teams. The role requires quota attainment, pipeline forecasting, market development, senior client relationships, and occasional domestic and international travel.

Leads mainframe modernization and migration initiatives for large global clients. Develops transformation and re-platforming solutions, conducts discovery workshops, presents technical and business cases, advises executives, and partners with sales, consulting, operations, and product teams. Requires extensive mainframe expertise across legacy technologies, migration architecture, data modernization, cloud and hybrid environments, and enterprise hosting. The role also supports AI-driven modernization, technical documentation, project lifecycle management, and client relationship development.

Build production-grade AI applications and agent capabilities using LLM APIs, agent frameworks, RAG pipelines, prompt and context engineering, and tool integrations. Develop reusable AI skills, multi-agent orchestration, and MCP connectors while applying strong Python and software engineering practices. Deploy services using Docker and Kubernetes, integrate databases and data pipelines, and collaborate across global projects and time zones.

Leads enterprise technology operations across service availability, infrastructure, security, resilience, incident management, business continuity, AI adoption, risk controls, budgets, vendors, and workforce planning. Owns operational governance, KPIs, investment priorities, audit readiness, and client-facing performance forums while building high-performing teams and driving automation, cost optimization, and measurable business value.

Develop and deploy enterprise AI and machine learning solutions for banking reconciliation platforms. Build agentic AI, Generative AI, LLM, and RAG applications; integrate full-stack systems; prototype emerging AI technologies; and move solutions from proof of concept to secure, scalable production. Responsibilities include writing production code, establishing technical standards, collaborating with cross-functional teams, and designing performance evaluation metrics for continuous optimization.