Top Artificial Intelligence Jobs in Chennai

Develop, deploy, and maintain machine learning and AI solutions, including predictive models, NLP applications, chatbots, LLMs, and agentic AI systems. Build scalable data and ETL pipelines using cloud platforms, big data technologies, and databases. Apply TensorFlow, PyTorch, Hugging Face, and cloud AI services while creating data visualizations and optimizing analytics workflows.

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.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Own the end-to-end product experience for agentic AI products, partnering directly with the CPO in a 0-to-1 startup environment. Responsibilities include roadmap and feature definition, UX optimization, product onboarding and conversion improvements, cross-module product mapping, data-driven prioritization, PRD and prototype creation, and collaboration with design and engineering. The role requires identifying friction, simplifying workflows, and translating product insights into onboarding, marketing pages, and in-app education.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

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.

Build and maintain Java backend services and end-to-end features (backend and frontend). Design data models and DB schemas, integrate with frontend, perform code reviews, troubleshoot, deploy with DevOps, and collaborate with US teams and partners to expand the supply-chain platform.

Lead discovery, rapid prototyping, and deployment of production-ready AI solutions for supply chain and manufacturing. Build Python-based RAG pipelines, semantic search, multi-agent workflows, data integrations, and enterprise platform connections. Engage frontline operations, translate business needs into technical requirements, manage agile delivery, and ensure prototypes are scalable and transferable to core engineering teams. Work across SAP, MES, shop-floor platforms, databases, telemetry, and unstructured data while collaborating with security, data, and architecture teams.

Develop, train, deploy, and maintain machine learning and AI models for predictive analytics, NLP, conversational AI, and automation. Build scalable solutions using cloud platforms, big data technologies, databases, ETL pipelines, visualization tools, LLMs, and agentic AI systems. Collaborate with a global client and adapt solutions to evolving AI requirements.

Lead AI/ML engineering vision and roadmap for healthcare and life sciences. Oversee multiple client engagements, design and deploy scalable production-grade AI solutions (ML, NLP, deep learning, computer vision, LLMs), ensure regulatory readiness, responsible AI practices, and drive adoption and value realization while managing executive client relationships and expanding AI adoption.

Develop and deploy Python services, REST APIs, data pipelines, machine learning models, and production GenAI/LLM features. Responsibilities include using Flask, Pandas, NumPy, SQL, and PyTorch; integrating and evaluating GPT, Llama2, and Gemini solutions; optimizing databases; writing tests and logging; and collaborating with product and data teams to deliver scalable AI applications.

Create realistic computer-use tasks for AI agents on Linux desktops. Responsibilities include designing multi-step scenarios, preparing files and documents, writing agent instructions, developing Python setup and scoring scripts, testing environments, and documenting tasks. Requires Python, daily AI tool usage, basic AI/ML experience, Ubuntu and virtual machine familiarity, attention to detail, and Git basics. Experience building RL environments or computer-use benchmarks is preferred.

Design and deploy production AI/ML systems, including LLM applications, RAG pipelines, automated model training, APIs, and multimodal data pipelines. Operationalize models with MLOps practices such as CI/CD, monitoring, versioning, and retraining. Build solutions on AWS using SageMaker, Lambda, S3, Glue, EKS, and Bedrock. Implement responsible AI guardrails, data governance, security, and compliance for sensitive enterprise data.

Lead technology vision and architecture for enterprise projects; design cloud-native, data-driven and Generative AI solutions; ensure scalability, security, governance, and engineering excellence; run architecture reviews, POCs, and implementations; evaluate emerging technologies; influence clients via workshops and executive engagements; mentor architects and distributed engineering teams.

Develop, test, and deploy AI agents and document-processing pipelines using AWS Bedrock, Python, AWS Fargate, OCR, and IDP technologies. Build integrations with Microsoft Graph API, implement prompts and guardrails, validate extracted data, and optimize agent workflows for regulated business processes. Collaborate with architects, QA teams, and stakeholders to deliver secure, compliant, production-ready AI solutions.

Coordinates the AI Accountability Network’s newsletters, training programs, grants, fellowships, payments, events, communications, and community initiatives. Manages editorial workflows, contractors, applications, logistics, websites, listservs, dashboards, metrics, and stakeholder communications. Supports grant administration, funder reporting, travel, curriculum development, and website publishing while improving processes and tracking program impact.

Build and refine AI-powered prototypes for supply chain and manufacturing operations. Engage with factory and warehouse teams, translate operational needs into software requirements, develop Python-based RAG and agentic systems, integrate APIs and data sources, and iterate prototypes based on frontline feedback. The role involves SQL and document data preparation, agile collaboration, occasional manufacturing-site travel, and progressive adoption of enterprise AI frameworks and production engineering practices.

Design, build, and scale production AI agents across EveryWatch. Own the agent roadmap, architecture, shared tools, memory, state, observability, evaluation, and cost controls. Harden WatchChat, develop RAG and tool-calling systems, establish measurable quality standards, and ship solutions directly in Python. Collaborate with sales, product, data, and engineering teams to identify high-impact use cases and take agent concepts from discovery through production.

Join Welo Data’s Canadian French contributor network for potential remote AI projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and details provided before each assignment.

Join Welo Data’s French AI Trainers Network as a flexible contributor supporting annotation, evaluation, and prompt creation projects. Contributors may label text, images, or audio; assess search results, advertisements, and chatbot responses; create prompts for large language models; and ensure cultural and linguistic accuracy. This is a talent community rather than an active opening, with project details and compensation provided when assignments become available.

Designs and implements end-to-end generative AI and LLM solutions, researches emerging AI techniques, develops supporting libraries and frameworks, and participates in software delivery from design through maintenance. The role applies NLP, computer vision, audio, and multimodal AI methods; uses cloud and open-source platforms; contributes to code reviews and roadmaps; and collaborates with cross-functional teams.

Lead the end-to-end product experience across acquisition, onboarding, avatar selection, chat, media generation, and retention. Drive conversion, retention, and LTV through hypotheses, prioritization, A/B testing, and data-informed design. Establish the product’s design direction and design system, lead a multidisciplinary design team, and partner with product, analytics, marketing, engineering, research, and support.

Lead technology strategy and governance for multiple business units, aligning IT investments with business goals. Drive digital transformation, cloud and automation adoption, risk management, service delivery, and cost optimization. Build and mentor high-performing teams, engage C-suite stakeholders, and ensure enterprise integration and compliance to deliver measurable business value across global operations.

Develop automation and AI solutions supporting pharmaceutical clinical trials. Interpret requirements, identify workflow improvements, build proof-of-concept tools using R, Shiny, and LLM-supported coding, and mature successful prototypes into validated, documented, reusable solutions. Collaborate with statisticians, programmers, data scientists, and AI leads while ensuring testing, traceability, audit trails, version control, and compliance with regulated clinical development expectations.

Designs and operationalizes enterprise analytics, KPI frameworks, data validation pipelines, machine learning solutions, and governed GenAI capabilities. Builds advanced SQL and Python data transformations, RAG and agentic AI workflows, evaluation frameworks, and production-ready deliverables. Applies anomaly detection, forecasting, and other advanced analytics techniques while implementing testing, logging, guardrails, deployment packaging, documentation, and security controls.