Top Artificial Intelligence Jobs in Chennai

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.

The ServiceNow Solution Architect - AI role involves leading and mentoring a team, designing and deploying AI-driven applications, managing application development, and integrating solutions into ServiceNow modules. Responsibilities include coordinating with stakeholders, troubleshooting, and ensuring adherence to technical standards.

Develop, train, deploy, and maintain machine learning and generative AI solutions, including predictive models, NLP applications, chatbots, LLMs, and agentic AI systems. Design scalable ETL pipelines, optimize databases, process large datasets with Spark and Snowflake, and create business visualizations. The role requires expertise across Python, SQL, cloud AI platforms, machine learning frameworks, databases, and data engineering.

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.

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.

Develop, fine-tune, deploy, and optimize AI/ML models and LLM-based RAG solutions using Python and AWS Bedrock. Build REST APIs and microservices with FastAPI or Flask, deploy scalable cloud infrastructure, manage containerized workloads, and develop data pipelines and ETL processes. Work with relational and NoSQL databases, CI/CD, and AWS services while collaborating with technical and product teams and documenting AI workflows.

Design, develop, and deploy production-grade generative AI and agentic systems, including RAG pipelines, autonomous agents, prompt architectures, and enterprise integrations. Build solutions using LangChain, LangGraph, vector databases, cloud services, APIs, and DevOps tooling. Advise stakeholders, document and monitor AI applications, mitigate technical and security risks, modernize RPA assets, and mentor junior developers.

Designs and leads enterprise AI Factory and GPU infrastructure architectures spanning compute, networking, storage, Kubernetes, cloud, security, observability, and AI/ML platforms. Assesses workloads, develops reference architectures and detailed designs, performs capacity planning and technology evaluations, leads proof-of-concepts, and supports deployment, migration, troubleshooting, and production transition. Collaborates across infrastructure, security, cloud, data, application, and operations teams to ensure scalable, secure, reliable, and cost-effective AI platforms.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.

Leads the organization’s data science and AI roadmap, managing a hands-on team that develops statistical models, experiments, forecasting, segmentation, production AI solutions, and agentic workflows. Drives GCP-based model deployment, MLOps/LLMOps, governance, observability, ethical AI, and adoption of AI tools. Partners with business, engineering, product, BI, and analytics teams to deliver practical data products, define success metrics, and communicate technical insights to executives.

Own the Workato environment, configure enterprise integrations, and build AI-native packages using Workato AI Genie. Design and deploy agent-ready workflows and AI-powered workflow automation solutions, while managing RBAC, auditing, and multi-workspace environments. Integrate Workato with enterprise applications and support scalable workflow implementations.

Partner with stakeholders to identify and prioritize AI use cases, map current and future business processes, assess feasibility and value, recommend build/buy options, and ensure solutions meet architecture, data, security, governance, and responsible AI requirements. Act as a bridge between business, IT, data science, and delivery teams.

Build production AI-powered automation, data pipelines, analytics, alerting, and anomaly detection systems. Integrate LLM features with internal tools, develop cloud and platform services, and collaborate with stakeholders to automate manual processes. Engineers specialize initially in AI, data, or platform engineering while contributing across tracks through pairing, code review, and shared delivery. The role requires strong API and data-handling skills, production engineering experience, clear communication, and willingness to work from the Chennai office during US Eastern overlap hours.

Creates technical documentation, implementation guides, whitepapers, product content, landing pages, and blog posts focused on AI infrastructure and enterprise technology. Researches complex technologies, translates specifications and architectures into clear content, collaborates with technical experts, validates accuracy, and maintains consistent documentation standards for IT professionals, engineers, architects, and technology decision-makers.

Own the CRM Opportunities backend end to end, designing scalable data models, APIs, distributed workflows, event-driven services, and background jobs. Improve performance and reliability across large multi-tenant pipelines while handling consistency, caching, sharding, observability, and incident response. Build AI-assisted CRM features using existing LLM APIs, including context assembly, response handling, cost and latency controls, and graceful degradation. Occasionally ship Vue.js UI changes and operate services through deployment and on-call.

Lead ServiceNow AI/ML development, mentor engineers, and architect Agentic AI solutions. Build, train, and deploy machine learning models, NLP and LLM applications, chatbots, autonomous agents, ETL pipelines, and large-scale analytics across AWS, Azure, and Google Cloud. Work with Spark, Snowflake, databases, visualization platforms, and ServiceNow to deliver intelligent workflow automation for global enterprises.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

The AI Engineer is responsible for designing, validating, and supporting engineering processes, ensuring compliance with development plans and customer requirements.

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

Support and enhance ADI's global compliance program aligned to enterprise risk and ISO 37301. Monitor regulatory developments, update policies, execute risk‑based monitoring and testing, manage investigations and remediation, support audits, develop training and communications, track metrics, and collaborate with Legal, HR, Finance, IT, and Operations to drive continuous improvement.

Sell and manage solution-level offerings in industrial automation, digital twin, AI and cybersecurity. Build customer relationships, promote Yokogawa technologies across industries, and support sustainable, technical solution adoption.

Creates and scales AI-enabled digital learning content for customers, partners, and internal teams. Produces product videos, tutorials, interactive modules, onboarding journeys, certifications, assessments, and enablement assets. Uses AI and multimedia tools for scripting, editing, translation, voiceover, graphics, and content repurposing. Partners with subject matter experts to simplify technical concepts, manages Academy publishing and content quality, and uses engagement analytics to improve learning experiences and self-service adoption.

Build enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry. Develop topics, triggers, routing, fallback handling, generative answers, RAG, knowledge grounding, citations, metadata-based retrieval, multilingual capabilities, and prompts. Integrate agents with Power Automate, SharePoint, Dataverse, Teams, Azure AI Search, and Azure OpenAI or Foundry models. Support troubleshooting, security, environments, ALM, and DEV/UAT/PROD deployments.

Build and deploy production AI systems across LLM orchestration, retrieval, agents, evaluations, voice, APIs, backend services, and infrastructure. Design reliable LLM pipelines, evaluation suites, data flows, and real-time multimodal experiences. Experiment with emerging models, debug issues across the stack, and communicate technical decisions clearly while owning projects from exploration through production iteration.

Work directly with pharmaceutical customers to understand workflows and design, prototype, integrate, and deploy AI-powered solutions. Translate ambiguous requirements into technical architectures, build Python-based integrations and data workflows, lead technical discovery and implementation workshops, debug APIs and cloud deployments, and collaborate with customer and internal engineering teams. Bring recurring customer needs to product and engineering while supporting regulated enterprise AI deployments.