Top Artificial Intelligence Jobs in Chennai

Supports product strategy, roadmap planning, feature prioritization, customer research, user-story definition, backlog management, and AI product delivery. Partners with engineering, marketing, and business stakeholders throughout the development lifecycle. Conducts market and competitive research, monitors AI trends, identifies product opportunities, and helps create documentation and go-to-market materials. The role targets recent MBA graduates interested in enterprise technology and AI-powered solutions.

Build and scale Bhumi’s college club ecosystem by forming and activating clubs, leading national campaigns, increasing volunteer participation, recruiting and developing student leaders, and tracking program performance through data and reporting. The role combines strategic planning, operational execution, outreach, engagement, recognition, and retention initiatives across colleges throughout India.

Own and scale strategic Global Systems Integrator partnerships, including joint business plans, go-to-market execution, governance, solution development, partner enablement, compliance frameworks, and performance measurement. The role coordinates Sales, Solutions, Marketing, Product, Engineering, Security, Legal, and Commercial teams to increase adoption, revenue impact, and scalable partner-led delivery.

Leads Innovation Lab research and applied technology innovation across IoT, AI, cloud, automation, digital services, and data-driven insights. Designs research frameworks, prototypes, evaluates projects, develops white papers and system models, builds partner ecosystems, facilitates co-innovation, and presents technology findings to internal and external audiences. Requires extensive IT experience, research leadership, industry networking, and strong communication skills.

Lead enterprise-wide AI transformation programs within the CIO/PMO organization. Responsibilities include partnering with senior executives, establishing program governance, managing Agile delivery cadence, risks, dependencies, milestones, and escalations, producing executive-level reporting, tracking PMO KPIs, building Jira and Confluence dashboards, and improving PMO processes across global, cross-functional teams.

Leads a global portfolio of process and analytics initiatives across pharmaceutical operations. Owns opportunity prioritization, value realization, project delivery, governance, risk management, stakeholder alignment, change adoption, and talent coaching. Uses process mining, automation, AI, and advanced analytics across SAP, Veeva, and Salesforce to improve efficiency, reduce risk, and deliver measurable financial and operational outcomes.

Design, test, and optimize prompts for AI models to improve output accuracy, relevance, efficiency, and consistency. Analyze AI-generated responses, collaborate with developers and business teams, create prompt libraries and documentation, and support AI-powered content generation, automation, and workflow initiatives.

Create and edit short-form, promotional, reel, and social media videos using AI-powered tools. Add voiceovers, subtitles, music, transitions, animations, captions, and visual effects while translating scripts into engaging content. Collaborate with content and marketing teams, meet deadlines, follow social media trends, and learn emerging AI video technologies.

Designs and implements AI-driven solutions for clients using Vendasta’s platform. Responsibilities include consulting on business objectives, configuring and validating AI workflows, troubleshooting, demonstrating solutions, training clients, and monitoring AI performance. The role also optimizes AI outputs, automates repetitive tasks, and translates complex AI concepts into business value for small and medium-sized businesses.

Develop AI and machine learning applications using cloud AI services and generative AI models. Implement deep learning, neural networks, chatbots, image processing, statistical analysis, and machine learning algorithms in production-ready systems. Independently contribute as a subject matter expert, collaborate with teams, solve technical problems, and remain current with AI and ML technologies.

Build and improve production LLM agents and AI features through evaluation, experimentation, observability, prompting, context engineering, tool use, and workflow design. Develop backend services, APIs, data models, and feedback pipelines that make agent behavior reliable and measurable. Investigate performance issues, run staged rollouts and production replays, define quality standards, and partner with Product, Data Science, and Sales to deliver business outcomes with appropriate privacy, security, and human-oversight safeguards.

Own the AI product roadmap from discovery through launch and iteration. Define model evaluation standards, failure modes, rollback plans, and agent-autonomy boundaries. Prototype and ship production AI features, influence architecture, and apply prompting and context design. Collaborate with Design, Research, Engineering, Sales, Marketing, and Customer Success while tracking quality, latency, cost, adoption, and growth metrics.

Design and develop production-ready agentic AI systems using Python, LangChain, LangGraph, and Amazon Bedrock. Build super-agent and sub-agent architectures, tool-calling workflows, RAG solutions, REST API integrations, and scalable cloud services. Implement prompts, structured outputs, state management, validation, retries, fallbacks, and guardrails. Test and evaluate agent performance, and deploy solutions within AWS environments using software engineering best practices.

Work on ML/DL and computer vision models for aerospace and defense (missiles, rocket telemetry, avionics). Train, test, and improve models; prepare and clean text/image/sensor datasets; build lightweight demo APIs with FastAPI/Flask; document experiments and collaborate with mentors on real-world avionics systems.

Develops and evaluates audiovisual generative AI technologies, prototypes proof-of-concept workflows, tests research developments, and builds scalable ComfyUI pipelines. The role manages training datasets, model tuning, inference environments, and custom nodes while collaborating with research, product, developers, and creatives. It also presents findings and trains artists across disciplines.

Designs, develops, and deploys production-ready AI/ML applications using Python and cloud AI services. Implements generative AI, deep learning, neural networks, chatbots, and image-processing solutions. Leads teams as an SME, makes cross-team technical decisions, mentors junior engineers, and coordinates cross-functional efforts to ensure scalable, reliable AI systems in cloud or on-prem pipelines.

Lead end-to-end payroll processing for India, including statutory filings, payments, compliance, exceptions, approvals, journals, and reconciliations. Serve as the country payroll subject matter expert, maintain data integrity, monitor KPIs and SLAs, improve processes, collaborate with cross-functional stakeholders, and mentor payroll specialists. Provide backup support to regional managers and operate effectively in a fast-paced, asynchronous international environment.

Lead global cloud operations across AWS, Azure, and GCP, managing distributed teams and 24x7 support. Responsibilities include service delivery, incident management, governance, security, monitoring, disaster recovery, business continuity, cloud financial operations, cost optimization, capacity planning, and executive reporting. The role requires maintaining highly available, reliable, secure, and compliant enterprise cloud platforms while standardizing operating models and operational processes.

Design, build, and deploy production-ready AI/ML applications using generative AI and cloud AI services. Lead solution architecture, integrate GenAI models, develop deep learning and image-processing components, maintain cloud and on-prem pipelines, and mentor teams while influencing cross-team technical decisions.

Own the architecture and delivery of an AI-assisted Nintex-to-Power Automate migration platform. Design LLM agents, prompts, evaluation harnesses, RAG or classification pipelines, parsers, and Azure integrations. Lead model selection, security, quality, migration risk, stakeholder management, and customer escalations. Develop Node.js/TypeScript or Python conversion engines, integrate APIs and authentication flows, validate workflow conversions, and mentor a 3–8 person AI and automation engineering team.

Architect generative AI workflows for cloud configuration and remediation using RAG, vector databases, and LLM APIs. Automate Terraform modules, Rego policies, and Regula test cases; oversee AI-enabled drift detection, discovery, gap analysis, and remediation capabilities. Lead cloud security tooling AI initiatives, design extensible service architectures, establish technical strategy, and mentor engineers across a distributed, asynchronous global team.

Lead the AI Capability Centre, driving strategic development and operational excellence in AI solutions while managing a team of AI experts.

Build production-grade agentic AI systems that analyze infrastructure discovery logs, execution results, errors, and failed discoveries. Develop AI agents for failure classification, root-cause analysis, troubleshooting, and corrective-action recommendations. Create telemetry data pipelines and REST API integrations, incorporate Gen AI APIs, RAG, and vector databases, and implement testing and guardrails for safe production changes. Maintain modern SDLC, CI/CD, testing, security scanning, release, and deployment practices.

Leads applications development and technology initiatives involving agentic AI integration and advanced Tableau solutions. Designs, develops, optimizes, governs, and troubleshoots dashboards and data integrations while translating business requirements into technical specifications. Partners with engineering and business stakeholders, supports data modeling and quality assurance, establishes development standards, resolves complex application problems, and mentors developers. Ensures architecture alignment, regulatory compliance, documentation, and effective project delivery.

Analyze large-scale transactional, operational, and customer datasets; build visualizations, machine learning models, and experiments; translate complex findings into clear recommendations for operational teams and executives; and apply analytical software, databases, visualization tools, and programming languages in a fast-paced blockchain environment.