Top Artificial Intelligence Jobs in Chennai

The role involves developing full-stack web apps, creating APIs, improving lead capture models, and collaborating across teams to enhance the Social Planner product.

Edit paid ads, social videos, UGC, product videos, podcasts, and website assets from raw footage through final delivery. Create fast-paced, high-retention short-form content, repurpose long-form footage, and produce multiple ad variations for testing. Use AI tools for voiceovers, captions, transcript editing, audio cleanup, reframing, clipping, and creative variations. Collaborate with social and creative teams to improve performance and stay current with AI editing tools and video trends.

Build and deploy production AI automations across accounting, compliance, sales, marketing, HR, and finance. The role covers discovery, coding, deployment, monitoring, debugging, support, ROI measurement, and business handover. The engineer will develop LLM-based systems, integrations, agents, and workflows using Python, APIs, databases, warehouses, and business tools. They will partner with non-technical stakeholders, independently own systems end to end, train business users, maintain infrastructure, and identify high-value automation opportunities.

Own the architecture and delivery of an AI-assisted migration platform that converts Nintex workflows into Power Automate flows. Responsibilities include LLM strategy, prompt engineering, RAG and classification pipelines, Azure cloud integration, parser and conversion-engine development, automated evaluation, security reviews, stakeholder management, risk mitigation, and mentoring a 3–8 person engineering team.

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

Build and operate production AI systems for financial products, including LLM pipelines, RAG, document extraction, anomaly detection, model serving, and automated reconciliation. Develop reliable backend integrations, vector search infrastructure, monitoring, evaluation, observability, and human-in-the-loop workflows. Collaborate with product, backend engineering, and data science teams to establish scalable AI engineering practices.

Design, develop, deploy, and optimize production-grade Generative AI and LLM-powered systems. Build agentic pipelines with orchestration, tool integration, feedback loops, asynchronous processing, and event-driven architectures. Collaborate across operations, marketing, customer experience, and product teams to identify AI opportunities. Troubleshoot systems, maintain documentation, communicate technical findings, and deliver scalable solutions using Python, cloud platforms, modern AI frameworks, and containerization.

Lead research and engineering work on LLM-based architectures for dialog, search, and destination entry. Optimize LLMs via prompting and fine-tuning, integrate them with ASR, TTS, and vision systems, and collaborate cross-functionally to deliver performant, resource-constrained solutions for automotive voice assistants.

Annotate first-person industrial and commercial videos by segmenting actions, assigning timestamps and labels, and describing hands, tools, objects, and workflows. Review and correct model-generated annotations, ensure complete and consistent coverage, resolve unclear cases, and meet quality and productivity targets. Prior hands-on experience with Physical AI, robotics, or industrial egocentric video annotation is mandatory.

Lead the design and production deployment of LLM-powered AI agents, generative AI systems, and retrieval-augmented generation infrastructure. Build and fine-tune models, develop multi-step agents, apply data science methods, and establish evaluation, monitoring, observability, and feedback systems. Partner with product, backend, and infrastructure teams to scale AI capabilities across messaging, scheduling, sales, and operations while influencing the AI roadmap and mentoring engineers.

Lead architecture for enterprise AI initiatives across products, platforms, and shared capabilities. Define AI architecture standards, reference patterns, governance, security, and operational guardrails. Guide solutions involving LLM applications, retrieval systems, agentic workflows, orchestration, evaluation, observability, and cloud-native platforms. Partner with business, product, and engineering leaders, influence strategic decisions, mentor technical teams, and support AI-enabled software development and platform modernization.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Build practical AI research systems for laboratory and customer use cases. Responsibilities include creating reinforcement learning environments, agentic systems, LLM pipelines, evaluation harnesses, benchmarks, and model-testing workflows. Run fine-tuning and model experiments, deploy local or self-hosted models, document reproducible results, and collaborate with GenAI researchers and cross-functional stakeholders to turn research concepts into reusable applications.

Own the backend and agent systems powering LLM products, including multi-turn conversations, tool calling, RAG, state management, evaluations, monitoring, and production reliability. Diagnose failures across models, knowledge systems, tools, and infrastructure; establish regression testing and quality measurement; and guide product and technical decisions. The role requires shipping and improving LLM features at scale, strong backend engineering, disciplined evaluation practices, and preferably Web3 experience.

Own the architecture and delivery of an AI-assisted migration platform converting Nintex workflows to Power Automate. Lead LLM strategy, prompt engineering, RAG and classification pipelines, Azure integration, testing, evaluation, security, risk management, and client stakeholder engagement. Mentor a 3–8 person AI and automation engineering team while building parsing and conversion engines in Node.js, TypeScript, or Python.

Own the end-to-end product experience for agentic AI products, partnering closely with the CPO, design, and engineering. Responsibilities include defining the roadmap, features, UX improvements, onboarding, in-app education, and product marketing content. The role requires deep product exploration, data-driven prioritization, identifying friction and gaps, creating comprehensive PRDs and prototypes, and shipping simple, high-impact improvements in a fast-moving startup environment.

Designs end-to-end enterprise architectures integrating AI/ML, Generative AI, automation, cloud services, data platforms, and production engineering. Translates client requirements into secure, scalable solutions; leads architecture reviews, proofs of concept, technology evaluations, and technical proposals. Defines integration and data strategies, guides MLOps/LLMOps adoption, advises stakeholders, ensures compliance and performance, and mentors engineering teams. The role also supports client-facing consulting and pre-sales solutioning.

Design, develop, and optimize scalable data platforms, pipelines, and processing workflows supporting analytics, business intelligence, and AI applications. Build solutions with Databricks and cloud technologies, integrate structured and unstructured data, implement quality and governance practices, optimize architectures, and support production deployment and monitoring. Collaborate with cross-functional teams to translate business requirements into reliable technical solutions and troubleshoot complex data challenges.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.

Implement machine learning models in production and design enterprise GenAI solutions, AI agents, and scalable LLM/ML automation. Build and manage industrialized processing pipelines, MLOps and LLMOps frameworks, and model lifecycle best practices. Integrate Microsoft Copilot Studio and Azure AI services with Power Platform tools, implement RAG and knowledge grounding, support multilingual retrieval, and troubleshoot agents. Gather requirements, estimate work, present technical solutions, and create documentation.

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.

Build and integrate AI agents, APIs, webhooks, databases, and business-system automations. Translate business processes and customer flows into simple, efficient workflows with minimal code. This is a remote, part-time individual-contributor consulting engagement requiring hands-on system building and immediate availability.

Designs and develops scalable cloud-based and serverless software across the company’s platform. Builds prototypes and production services, applies AI throughout the SDLC, promotes modern Agile and Well-Architected practices, troubleshoots customer and internal issues, develops comprehensive test suites, and coaches software engineers. The role requires extensive distributed application experience and backend expertise across multiple programming languages, databases, cloud technologies, integrations, and workflow orchestration.

Evaluate datasets and AI-generated outputs for accuracy, relevance, consistency, and quality. Provide written feedback to improve AI system performance, follow project guidelines, support quality assurance, and communicate findings clearly. The role requires strong critical thinking, analytical reasoning, attention to detail, organization, and independent work in a remote environment. Prior AI experience is not required; data annotation, content evaluation, quality assurance, research, and AI project experience are preferred.