Top Artificial Intelligence Jobs in Chennai

Design, develop, and operate scalable cloud-native systems using Go, concurrency, distributed systems, REST APIs, microservices, SQL, AWS, Docker, and Kubernetes. Lead releases, CI/CD, testing, troubleshooting, observability, and production improvements. Use and govern AI-assisted development tools, review AI-generated outputs, mentor engineers, improve team workflows, and collaborate with Product, QA, DevOps, and operational stakeholders.

Plans and coordinates cross-functional AI/ML, software, and data projects from concept through production using Agile and NPI frameworks. Manages roadmaps, backlogs, risks, dependencies, stakeholder communication, project reporting, KPIs, business impact, and savings validation. Facilitates Scrum ceremonies, supports data and model delivery, tracks financial status, and drives continuous improvement across innovation projects.

Design, develop, deploy, and integrate AI, machine learning, and generative AI solutions for industrial and business applications. Own the machine learning lifecycle, define architectures, assess data readiness, monitor production models, and ensure reliability and maintainability. Evaluate emerging AI technologies, manage technical delivery from partners and contractors, and collaborate with cross-functional stakeholders to identify use cases, support adoption, and deliver measurable operational improvements.

Supports enterprise software implementations for maritime and terminal operations customers. Responsibilities include system analysis, configuration, integration, issue verification, case creation in Salesforce, requirements analysis, test-case preparation, and AI-enabled consulting support. The intern communicates with customers and internal teams, manages assigned issues, contributes to project deliverables, and learns supply chain software, consulting practices, and approved AI tools under senior guidance.

Conduct applied and fundamental research in reinforcement learning, agentic AI, multimodal reasoning, and foundation-model adaptation for semiconductor inspection and metrology. Develop intelligent systems that plan, use specialized tools, learn from feedback, and make sequential decisions. Design rigorous experiments, evaluate reliability and failure recovery, build reproducible prototypes, and translate research into scalable industrial solutions. Collaborate with multidisciplinary teams and manage engineers working in AI.

Leads AI engineering teams and builds scalable enterprise AI platforms supporting generative and agentic AI applications. Oversees platform architecture, deployment, reliability, governance, model lifecycle management, KPIs, and engineering standards. Develops global teams, mentors technical talent, manages strategic initiatives, and advises stakeholders on AI solutions. Drives responsible AI practices, innovation, partnerships, and adoption of emerging technologies across the enterprise.

Leads technology strategy and planning programs focused on generative AI and enterprise architecture. Develops technology roadmaps, scalable LLM and NLP solutions, Agentic and non-Agentic AI applications, data pipelines, and LLMOps capabilities. Partners with architects, business stakeholders, vendors, and senior leadership to prioritize products, evaluate emerging technologies, design solutions, and deliver engineering excellence initiatives. Applies advanced RAG, RLHF, prompt management, validation, and automation to transform unstructured data into business value.

Lead the design, development, and deployment of production-grade generative AI and agentic systems, including LLM workflows, RAG pipelines, vector database integrations, and AI governance. Build scalable Java and Python backend services, cloud-native microservices, and customer-facing applications. Monitor model performance and costs while leading teams, managing budgets, and delivering intelligent experiences at enterprise scale.

Leads quality engineering for agentic AI and RAG systems, covering end-to-end testing, automated evaluation, safety and security assessments, performance and reliability testing, red teaming, data validation, observability, and production readiness. The role validates agent reasoning, tool use, retrieval quality, guardrails, privacy, resilience, scalability, and cost efficiency while establishing test frameworks, SLOs, and continuous regression processes.

Design and lead enterprise-grade AI agents, data pipelines, ELT/ETL processes, microservices, and data federation layers. Build generative AI solutions using Google ADK and LLMs, integrate agentic AI tools, and deploy scalable applications across cloud-native, Kubernetes, OpenShift, and CI/CD environments. Ensure data quality, security, resilience, and compliance while advancing data engineering standards and supporting risk technology initiatives.

Develop and support enterprise applications using Java microservices, Angular, RESTful APIs, and modern development tools. Lead application analysis, design, testing, implementation, security, and ongoing support across the SDLC. Estimate project costs and timelines, advise stakeholders, ensure software quality and compliance, and mentor junior analysts. The role also contributes to Gen AI and Agentic AI implementations within financial services, requiring knowledge of banking, wealth management, CRM, and investment products.

Develop and deliver scalable, cloud-native Java applications for FX and Equities Prime Brokerage. Design system architecture, implement and test features, modernize legacy platforms, improve development practices, and provide third-line production support. Collaborate with trading, quantitative, product, and technology teams to build data-driven solutions. The role also involves mentoring junior engineers, applying AI coding tools, working with messaging, databases, caching, and ultra-low-latency technologies, and maintaining operational documentation and standards.

Lead application development and architecture initiatives using Java, microservices, generative AI, cloud-native platforms, event-driven systems, and enterprise data technologies. Define scalable application designs, coding and testing standards, CI/CD and security practices, and integration strategies. Resolve complex technical and business-process problems, advise management, assess risk and compliance, and mentor developers and analysts while guiding cross-functional engineering teams and delivering robust solution architectures.

Leads application development and architecture for scalable Java microservices and generative AI solutions. Partners with technology and management teams, defines system enhancements, establishes coding and testing standards, resolves complex technical problems, and ensures architecture alignment. Designs cloud-native, event-driven systems; guides security, DevOps, data, and deployment practices; assesses risk and compliance; and mentors developers and analysts.

Designs and delivers production-grade generative AI solutions using LLMs, RAG pipelines, agentic workflows, APIs, and Azure technologies. Responsibilities include architecture, coding, testing, deployment, evaluation, monitoring, governance, cost optimization, and responsible AI practices. The role translates business problems into measurable AI outcomes, collaborates with technical and business stakeholders, supports proposals and proofs of concept, develops reusable frameworks, and contributes to code reviews, mentorship, knowledge sharing, and internal AI capabilities.

Engineer and operate enterprise AI platforms across Microsoft Copilot, Copilot Studio, Power Platform, and Azure AI. Responsibilities include platform governance, agent lifecycle management, deployment automation, DLP and access controls, enterprise integrations, monitoring, reporting, cost controls, and remediation of ungoverned AI assets. The role partners with Security, IAM, Architecture, Service Management, and FinOps teams to establish secure, scalable, and automated AI platform operations.

Build and deploy AI-powered applications, LLM solutions, and agentic workflows for real-world business problems. The role combines backend engineering with rapid prototyping, enterprise integrations, AI orchestration, RAG, vector databases, and production deployment. Responsibilities include developing scalable applications, collaborating with clients and cross-functional teams, integrating AI into backend systems, and transitioning proof-of-concepts into production-ready solutions.

Builds and operates secure, scalable cloud infrastructure for generative AI applications. Responsibilities include AI/LLMOps pipelines, CI/CD automation, infrastructure as code, Docker and Kubernetes orchestration, observability, SRE practices, security governance, and developer enablement. The role supports reliable production deployment, monitoring, evaluation, and lifecycle management of AI workloads across Google Cloud.

Develop and deploy enterprise AI/ML solutions for banking reconciliation platforms. Responsibilities include building agentic AI and Generative AI applications, integrating LLMs and machine learning tools, prototyping emerging AI technologies, writing production code, deploying secure cloud-native services, and evaluating model and agent performance. The role requires expertise in Python, LLMs, RAG, vector databases, GenAIOps, Docker, and AI system architecture.

Designs, develops, deploys, and monitors AI and machine learning solutions, including predictive models, NLP applications, LLMs, generative AI, RAG systems, copilots, chatbots, and virtual assistants. Responsibilities include building ML pipelines, creating APIs and microservices, integrating AI with enterprise and cloud platforms, applying MLOps practices, and ensuring model scalability, security, governance, compliance, and reliability.

The Product Manager will define and drive AI-powered statistics for life sciences, collaborate across teams, engage with customers, and support market differentiation strategies.

Develop and execute AEO and GEO strategies to increase visibility, citations, referral traffic, and conversions across AI search platforms. Conduct technical SEO audits, optimize content and site architecture, monitor AI visibility, design experiments, build attribution dashboards, and collaborate with engineering, product, editorial, and marketing teams. Own AI search KPIs and adapt strategies to evolving crawler, platform, and citation behaviors.

Build and operate production-grade LLM applications, agentic workflows, RAG and retrieval pipelines, AI memory architectures, and evaluation harnesses. Deploy and monitor AI services using cloud infrastructure, containers, APIs, and MLOps platforms. Configure AI gateways, manage model routing and costs, secure integrations, and optimize serving architectures. Collaborate with product, engineering, SRE, security, and software teams to deliver scalable AI capabilities.

Build responsive, scalable web applications with reusable UI components and modern front-end architectures. Integrate LLMs and generative AI capabilities, including chatbots, copilots, recommendation engines, intelligent search, RAG workflows, and AI agents. Collaborate with product, UX, data science, and backend teams; integrate APIs and microservices; participate in architecture decisions, code reviews, Agile delivery, and monitoring of AI-powered experiences.

Owns the enterprise architecture and hands-on implementation of Coursera’s agentic AI foundation. Designs agent orchestration, context and semantic architectures, governance, security, CI/CD automation, cost optimization, integrations, observability, and production standards. Enables business teams to build reliable AI-native workflows at scale while partnering with Engineering, IT, Data, Security, and business pods. The role also includes hiring, coaching, technical leadership development, and defining operating models and adoption metrics.