Top Artificial Intelligence Jobs in Chennai

Build production-grade AI applications for pharmaceutical commercial, medical, and patient workflows using LLMs, retrieval, agents, structured data, and multimodal AI. Develop scientific knowledge systems, evaluation frameworks, and reliable, safe AI solutions using healthcare and life sciences datasets. Collaborate with pharma customers and domain experts to translate complex workflows into scalable technical products.

Build AI-powered business solutions using Microsoft Copilot, Copilot Studio, Power Apps, Power Automate, SharePoint, AI Builder, and Dataverse. Develop Copilot agents, automated workflows, prompt libraries, and reusable AI accelerators; support governance, security, responsible AI, and enterprise adoption. Evaluate emerging technologies, create prototypes, deliver training and workshops, and collaborate with business and technology stakeholders.

Supports the creation, publishing, translation, editing, and quality assurance of AI-generated educational content. Uses generative AI and multimedia tools to produce learning experiences, refine text, images, audio, and video, and publish materials in an LMS. Conducts manual accuracy and quality reviews, applies instructional design principles, explores efficiency improvements, monitors emerging AI trends, and provides feedback to improve internal content development tools.

Evaluate AI-generated technical content, craft and answer advanced ML/AI prompts, assess model responses for accuracy, relevance, and clarity, identify errors or hallucinations, and provide feedback to improve AI systems.

Leads OneMagnify’s AI practice by guiding client strategy, directing generative AI architecture and delivery, and translating business needs into production-ready solutions. Manages and mentors data scientists, AI engineers, and software engineers while overseeing Databricks-based data pipelines, model deployment, MLOps, and LLM applications. Partners across analytics, strategy, engineering, creative, and operations teams, presents technical concepts to executives, and contributes to evolving the company’s AI service offerings.

Build AI systems and solutions using machine learning, deep learning, and large language models. Prototype models with Python and common ML libraries, develop deep learning architectures, fine-tune transformer models, and implement retrieval-augmented generation using vector databases. Apply feature engineering, model selection, optimization, and validation techniques while working with SQL, data pipelines, Git, Jupyter, and Docker. Communicate technical trade-offs clearly and own experimentation and problem-solving efforts.

Lead the architecture and development of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems for intelligent networking. Drive the full software lifecycle from design through production operations, establish technical direction, mentor engineers, and improve system reliability, security, observability, performance, and scalability.

Lead the technical vision, architecture, development, testing, deployment, and operations of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Build secure, resilient, observable services processing high-volume network data; guide technical strategy, improve engineering practices, and mentor engineers. Translate complex technical challenges into production-ready solutions across intelligent networking, cloud platforms, analytics, and automation.

Leads the architecture, development, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic systems for intelligent networking. Drives technical vision, builds secure and observable production services, mentors engineers, leads technical discussions, and improves system reliability and performance. Requires extensive software engineering and distributed systems experience, proficiency in a general-purpose programming language, cloud expertise, and familiarity with AI, stream processing, network telemetry, or large-scale analytics.

Leads the architecture, development, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems. Defines technical strategy, builds secure and observable production services, processes high-volume network data, and mentors engineers. The role requires hands-on software engineering leadership across the full lifecycle, including design, coding, testing, cloud deployment, operations, and technical reviews.

Lead the architecture, development, testing, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems. Define technical vision, build secure and observable production services, process high-volume network data, mentor engineers, and guide technical strategy across intelligent networking and cloud-native platforms.

Lead the architecture and development of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic systems for intelligent networking. Drive the full software lifecycle, establish technical direction, mentor engineers, and ensure production systems are secure, observable, resilient, and highly performant.

Lead the architecture, development, testing, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Define technical vision, build secure and observable high-performance services, process large-scale network data, and integrate intelligent networking capabilities. Mentor engineers, lead technical discussions and reviews, improve system reliability and performance, and translate complex technical challenges into production-ready solutions.

Lead the architecture and development of scalable, secure, observable AI infrastructure, distributed microservices, real-time data pipelines, and agentic systems for intelligent networking. Drive technical vision, software delivery, system reliability, and operational excellence while mentoring engineers. Build production platforms processing high-volume network and operational data, integrating cloud infrastructure, machine learning, telemetry, and automation capabilities.

Develop scalable enterprise applications and REST APIs using Python or .NET/C#, integrate Azure and Microsoft Fabric services, build SQL-based data and ETL/ELT solutions, and support Lakehouse, OneLake, and Data Warehouse architectures. Integrate LLMs and RAG capabilities into enterprise applications, troubleshoot application and data-pipeline issues, and collaborate with product, data, AI, and business teams. Backend and integration development are the primary focus, with working knowledge of modern front-end frameworks expected.

Design, develop, test, and deploy cloud-native microservices and APIs that run ML/AI algorithms on structured and unstructured telemetry data. Contribute code, automated tests, CI/CD pipelines, containerized deployments, and documentation while collaborating with product teams and geo-distributed stakeholders.

Designs, operates, and improves scalable cloud infrastructure across GCP and AWS. Builds Terraform-based infrastructure, GitHub Actions CI/CD pipelines, observability, automation, and AI-enabled monitoring and remediation. Manages MySQL and SQL Server databases, Cloudflare services, security controls, repository governance, and incident response. Supports reliability, performance, cost optimization, compliance, and cloud modernization while providing 24/7 on-call coverage and mentoring junior engineers.

Architect and build production-grade generative AI and agentic AI applications, including LLM workflows, multi-agent systems, RAG pipelines, APIs, and ML/GenAI infrastructure. Develop and optimize models, manage deployment and monitoring, apply responsible AI practices, and collaborate directly with customers. The role also involves mentoring engineers and shaping AI platform strategy.

Build and deploy production-ready AI agents and multi-agent systems on Azure. Design RAG pipelines, orchestration patterns, tool integrations, APIs, prompt engineering workflows, evaluation loops, and unstructured-data pipelines. Implement CI/CD, monitoring, responsible AI controls, and enterprise integrations with Microsoft Teams and M365 Copilot. The role requires hands-on GenAI/LLM engineering, Python development, cloud infrastructure expertise, and flexibility across data engineering, low-code configuration, and AI-native development tools.

Design, build, test, and deploy production-grade agentic AI systems using Python, LangGraph, LangChain, AWS Bedrock, and orchestration tools. Integrate AI agents with APIs and business systems, implement memory and retrieval, establish observability and safeguards, and develop reusable SDKs and templates. Collaborate directly with clients to define requirements, run experiments, and rapidly deliver reliable AI solutions using modern software engineering, cloud deployment, and CI/CD practices.

Designs and maintains a centralized AI platform and agentic framework for secure enterprise deployment and access. Builds scalable AI agents, unified APIs, distributed systems, and microservices; integrates agents into customer and internal applications; optimizes cloud-native workloads; ensures security and regulatory compliance; and leads technical strategy, mentoring, performance analysis, and capacity planning.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

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.

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.