Top Artificial Intelligence Jobs in Chennai

Supports customers through live chat by troubleshooting wireless internet products and services, resolving issues, and delivering positive customer experiences. The role requires energetic, meaningful customer engagement and focuses on helping clients’ customers while contributing to customer experience insights and digital support operations.

Build and maintain backend services and APIs for a media assets platform, including permissions, CDN delivery, image and video processing, usage tracking, and AI-powered media features. Use AI tools and agents throughout the software development lifecycle, while owning design, testing, code review, debugging, documentation, production operations, and incident response. Collaborate across teams and mentor engineers on responsible AI-assisted development.

Lead the design, development, and deployment of AI and machine learning systems for promotion optimization, price elasticity, demand forecasting, and personalized recommendations. Build scalable forecasting models, causal-inference solutions, dynamic pricing systems, ML pipelines, APIs, and monitoring workflows. Collaborate with data engineering, product, marketing analytics, and cloud teams while ensuring secure, stable, and cost-effective production deployments.

Own reliability engineering for a multi-tenant SaaS platform, including SLOs, observability, production on-call, incident response, CI/CD, progressive delivery, Helm, Kubernetes operations, capacity planning, and automation. Lead releases, postmortems, and toil reduction while partnering with infrastructure security teams. Establish guardrails for AI-generated deployment code, protect secrets and customer data, mentor engineers, and improve availability, deployment speed, and developer experience.

Creates and manages content across social media, blogs, newsletters, executive communications, and marketing campaigns. Supports brand storytelling, thought leadership, industry positioning, and demand generation. Uses generative AI and automation tools to improve content workflows and productivity. Manages publishing schedules, researches trends, monitors engagement and campaign performance, collaborates with Sales, and ensures accurate, consistent brand messaging.

Build production agentic AI solutions and reusable frameworks on Azure AI Foundry. Responsibilities include multi-agent orchestration, RAG knowledge repositories, reusable tools, Dataverse and Power Platform integration, governance, human approvals, evaluation, observability, security, and infrastructure-as-code deployments. The role requires strong Python and C#/.NET engineering skills, production LLM application experience, Azure expertise, and collaboration with business, technical, and risk stakeholders.

Designs and delivers enterprise AI solutions on SAP BTP using SAP AI Core, AI Launchpad, Joule, ML services, and SAP Build Process Automation. Responsibilities include developing AI/ML models, integrating Joule with SAP business applications, delivering intelligent workflows, defining AI architecture and MLOps pipelines, and creating Clean Core-aligned extensions across multi-cloud environments.

Build and operate AI research products involving hybrid retrieval, paper-to-video matching, RAG, agentic pipelines, protocol understanding, and cited scientific answers. Develop evaluation frameworks, production LLM services, APIs, MCP integrations, and search instrumentation. Collaborate with product teams, scientists, editors, and engineers while optimizing relevance, citation accuracy, latency, and cost.

Own and lead the company’s end-to-end legal function, including drafting and negotiating commercial agreements, advising on contractual risks, managing vendors and external counsel, supporting employment and equity matters, and handling fundraising, governance, IP, privacy, compliance, and regulatory issues. The role requires strong US commercial contract expertise and experience supporting US-based companies, ideally in pharma, life sciences, or healthcare.

Build and operate internal AI platforms for developer workflows and AI-assisted operations. Integrate AI into CI/CD and engineering toolchains, implement quality and compliance guardrails, support HIPAA/GxP and FedRAMP-boundary environments, and develop anomaly detection, incident triage, runbook automation, and self-healing capabilities. Partner with engineering, security, compliance, and leadership to establish responsible AI standards and measure improvements in engineering throughput and operational reliability.

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.

Creates educational videos by transforming scripts, storyboards, and animatics into AI-generated footage. Uses prompt engineering, generative AI image and video tools, editing, compositing, motion graphics, and traditional animation techniques. Maintains visual consistency, character continuity, and production quality while correcting AI artifacts. Collaborates with creative leads, illustrators, and content developers, manages multiple projects, meets deadlines, and incorporates feedback.

Leads the FP&A technology and analytics function, owning the technology roadmap, planning systems, reporting, data governance, integrations, automation, AI, and transformation delivery. Oversees Workday Adaptive Planning, Power BI, Snowflake, and related finance data architectures while partnering with Finance, IT, Data, Engineering, HR, and Accounting. Establishes governance, controls, adoption programs, and scalable self-service analytics, while managing and developing FP&A technology, BI, systems, and analytics teams.

Leads the architecture, development, and deployment of agentic AI, LLM, and traditional machine learning solutions. Designs production systems using Python, LangChain, LangGraph, MCP, APIs, databases, and Kafka. Translates business needs into scalable architectures, establishes AI evaluation and observability frameworks, promotes privacy and governance, mentors engineers and data scientists, and drives proofs of concept and emerging AI initiatives across enterprise projects.

Build production-grade agentic AI systems that reason, search, and execute workflows. Develop RAG pipelines, AI agents, semantic search layers, data processing solutions, dimensional data models, and scalable AI workloads. Work with structured and unstructured data, LLMs, vector databases, cloud infrastructure, CI/CD, and ML observability. Responsibilities include fine-tuning models, prompt engineering, optimizing context windows, and deploying reliable AI applications.

Design, develop, and deploy production-grade AI applications focused on agentic AI, multi-agent systems, RAG, and cloud solutions. Build Python and FastAPI backends, integrate LLMs and enterprise data, deploy applications with Docker and Kubernetes, and implement evaluation, observability, security, reliability, and responsible AI practices. Collaborate with product, engineering, and business teams to deliver scalable AI solutions.

Develop, train, fine-tune, evaluate, and deploy machine learning models using large datasets. Responsibilities include data preprocessing, feature engineering, deep learning, NLP and LLM research, predictive analytics, production API integration, experimentation, and model optimization. The role collaborates with software engineers, supports MLOps practices, and evaluates model accuracy, efficiency, latency, and performance.

Develop R 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 automation leads while ensuring compliance with clinical development standards, including testing, traceability, audit trails, version control, and regulated delivery expectations.

Develops sales decks, proposals, capability presentations, RFP/RFI responses, and sales enablement content. Conducts industry research, translates technical concepts into business-focused messaging, and collaborates with Sales, Marketing, Delivery, design teams, and subject matter experts. Supports client-facing presentations and manages content projects independently in a fast-paced environment. Requires strong PowerPoint, research, writing, presentation development, and stakeholder communication skills, along with familiarity with IT services and technologies such as cloud, AI, and data analytics.

Design, build, and scale production AI agent workflows using AWS Bedrock AgentCore, Step Functions, Claude, MCP, RAG, and guardrails. Develop Python/FastAPI backends, agent-facing interfaces, document-processing pipelines, enterprise integrations, and AWS infrastructure. Implement observability, evaluations, security controls, human approval workflows, and CI/CD. Work directly with enterprise customers and nontechnical stakeholders to deliver end-to-end AI solutions with minimal guidance.

Leads enterprise AI architecture and transformation across hybrid and multi-cloud infrastructure environments. Designs Generative AI, AIOps, automation, governance, security, and self-healing infrastructure solutions using platforms such as Azure, AWS, and GCP. Develops RAG architectures, AI assistants, and operational workflows integrated with ServiceNow and enterprise systems. Advises executive stakeholders, leads large transformation programs, and supports solutioning, presales, governance, and customer-facing consulting.

Design and develop enterprise generative AI and RAG applications using LLMs, embeddings, vector databases, hybrid search, and agentic workflows. Build ingestion, retrieval, evaluation, observability, and secure API services; optimize accuracy, latency, scalability, and cost. Integrate AI solutions with enterprise systems and collaborate with technical and business stakeholders. Apply prompt engineering, context management, guardrails, security, and responsible AI practices while troubleshooting and improving production applications.

Lead the architecture and development of production-grade multi-agent systems and AI-powered security chatbots. Build applied LLM workflows for alert triage, summarization, and automation; create evaluation harnesses; monitor and improve production behavior; establish standards for retrieval, context engineering, tool design, and safety; and embed security controls such as privacy, access controls, and prompt-injection defenses. Collaborate across data science, engineering, product, and platform teams to move AI capabilities from prototype to production.

Evaluate and improve the safety, security, and robustness of large language models. Responsibilities include designing jailbreak, prompt injection, adversarial, and tool-use abuse tests; developing regression suites and evaluation frameworks; identifying vulnerabilities; translating findings into safety recommendations; and documenting methodologies and results for technical and non-technical stakeholders.

Build and own Supabase’s internal AI execution platform, including event-driven agent runtimes, durable state, human review, rollback, observability, evaluations, governance, and least-privilege tooling. Develop production agents, MCP integrations, infrastructure on Python and GCP, CI evaluation gates, operating metrics, and return-on-investment instrumentation. As the sole platform engineer, independently make architecture decisions, operate systems end to end, and ensure unsafe actions are structurally impossible.