Top Artificial Intelligence Jobs in Chennai

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.

Build, maintain, troubleshoot, and QA responsive websites using WordPress, Website Pro, and other CMS platforms. Handle complex layouts, widgets, plugin issues, and WooCommerce integrations. Use AI tools and prompt refinement to improve content, design, and production workflows. Lead complex Zendesk tickets, communicate with clients, mentor junior teammates, apply SEO and accessibility practices, configure analytics and tag managers, and recommend user-experience and conversion improvements.

Design, prototype, and deploy AI/ML and computer-vision solutions for production factories. Perform feasibility studies, collaborate globally with research teams, support operational excellence, and integrate systems with existing hardware to improve production processes.

Build and deploy regulated-process AI agents on AWS Bedrock, including document generation, validation, and case intake workflows. Develop Fargate services, integrate Microsoft Graph API and event-driven patterns, configure prompts and guardrails, and test agent performance. Own OCR and intelligent document-processing pipelines using AWS Textract or equivalent and Azure Document Intelligence. Improve extraction accuracy, support auditability, integrate third-party tooling, and collaborate with QA and compliance teams.

Develop, test, and deploy generative AI agents and document-processing pipelines using AWS Bedrock and Fargate. Build OCR and intelligent document processing workflows, integrate Microsoft Graph API and enterprise platforms, engineer prompts and guardrails, and improve extraction accuracy and agent performance. Collaborate with architects, QA, and business stakeholders to deliver secure, compliant, production-ready AI automation for regulated and semi-regulated processes.

Lead a backend engineering team building scalable Java services and APIs for AI-powered applications. Architect microservices, integrate and deploy machine learning models, oversee full-lifecycle software delivery, and enforce testing, code quality, and CI/CD practices. Collaborate with data scientists, product managers, and engineering stakeholders while driving technical innovation, mentoring developers, and shaping architecture and delivery strategy.

Develop backend services and APIs for enterprise AI and agentic platforms. Build MCP servers, authentication and authorization frameworks, API governance capabilities, and orchestration workflows. Integrate tools, data sources, agents, and LLMs while securing and governing production systems. Collaborate with architecture, security, and platform teams to deliver scalable AI infrastructure.

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.

Design, develop, and maintain enterprise-scale, componentized applications using Java/Spring and modern Angular UIs. Partner with stakeholders to define enhancements, resolve complex issues, enforce coding standards, mentor junior staff, and support CI/CD and cloud-ready deployments while ensuring compliance and risk management.

Sets technical direction for enterprise AI automation across RPA, Playwright, and agentic AI systems. Owns automation architecture, platform strategy, RPA modernization, migration decisions, engineering standards, reliability patterns, and AI integrations. Builds reusable tooling and CI/CD components, leads architecture reviews, mentors engineers, and serves as the technical escalation point. Uses telemetry to guide prioritization and platform investments while partnering with cross-functional stakeholders on governance and enterprise-scale automation.

Designs, deploys, and supports intelligent automation across IT and business operations. Responsibilities include building Playwright, API-driven, RPA, and AI-agent workflows; maintaining live automations; integrating LLM platforms; analyzing business processes; implementing telemetry, error handling, documentation, and governance; and advancing the organization from legacy RPA toward agentic automation.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

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.

Design, build, test, and ship production AI-enabled software and reusable platform components. Integrate AI services, APIs, retrieval patterns, and business logic. Collaborate with U.S. partners, write tests, participate in code reviews and on-call rotation, debug production issues, and mentor junior engineers.

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.

Designs and maintains a centralized AI platform and agentic framework supporting secure deployment, management, and integration of AI agents. Responsibilities include building scalable agentic solutions, developing unified APIs, integrating agents into applications, optimizing cloud-native workloads, ensuring security and compliance, conducting capacity planning, mentoring engineers, and shaping the platform roadmap.

Leads the architecture, development, deployment, and governance of enterprise AI systems, including LLM applications, multimodal models, RAG, and multi-agent solutions. Drives prototyping through production delivery, establishes LLMOps practices such as CI/CD, observability, evaluation, and model versioning, and supports presales strategy. Collaborates with product and data teams, ensures security and privacy compliance, and mentors junior engineers.

Lead the ethical use and governance of AI, ensuring compliance with legal standards, managing AI risks, and fostering collaboration across teams.

Lead end-to-end delivery of AI, machine learning, and GenAI initiatives using Agile, Scrum, or hybrid methods. Manage cross-functional teams, project scope, resources, schedules, risks, budgets, dependencies, and governance. Partner with business and IT stakeholders to identify AI use cases and measure benefits. Lead steering committees, executive reviews, demos, portfolio planning, and leadership reporting while ensuring compliance with architecture, security, data protection, and enterprise governance standards.

Improve client discoverability through on-page, off-page, local, and technical SEO. Conduct keyword research, content mapping, backlink analysis, competitor research, and performance reporting using SEO and AI tools. Optimize content for organic search, AI-powered discovery, voice search, and local results. Support link-building, audits, A/B testing, dashboards, prompt refinement, and cross-functional SEO initiatives while following established processes.

Leads the architecture, development, and deployment of large-scale AI/ML, generative AI, and agentic systems. Responsibilities include designing end-to-end ML pipelines, fine-tuning and evaluating LLMs, mentoring data science and engineering teams, establishing secure and ethical AI practices, and providing technical thought leadership. The role also covers model monitoring, MLOps, distributed infrastructure, conversational data analysis, cross-functional collaboration, and adoption of emerging AI technologies.

Build and deploy end-to-end data infrastructure and customer-facing solutions for generative AI applications. Collaborate directly with technical customers and AI researchers, translate business needs into engineering solutions, rapidly experiment, and deliver scalable front-end, back-end, and infrastructure features. The role also involves influencing product direction, engineering culture, and critical projects supporting enterprise and government AI initiatives.

Conduct AI visibility audits across ChatGPT, Perplexity, and Google AI Mode; identify content, schema, and technical improvements; use AI tools to run scripts; prepare client reports and updates; and document repeatable processes. The role involves live client work, communication, experimentation, and learning SEO, analytics, and AI visibility tools on the job.

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 ship AI-powered features for fashion, beauty, and home using large-scale product, image, and consumer data. Prototype with LLMs, agents, multimodal and CV models, run experiments, measure impact, and take models end-to-end from hypothesis to production while collaborating with product, design, and business teams.