Top Artificial Intelligence Jobs in Chennai

Leads the architecture, development, and deployment of large-scale AI/ML, generative AI, and agentic systems. Responsibilities include designing data pipelines and model architectures, fine-tuning and evaluating LLMs, mentoring AI teams, establishing MLOps and validation best practices, ensuring secure and ethical deployments, and partnering with product and business stakeholders. The role also provides technical thought leadership, analyzes conversational data, and drives scalable AI innovation.

Leverage generative AI and AI-enhanced platforms to improve productivity, streamline workflows, support data-informed execution, and apply AI capabilities responsibly while understanding their limitations and ethical considerations.

The posting provides no specific responsibilities or qualifications beyond the Data Science Manager title. The role appears associated with Accenture’s Data and AI services and resources practice.

Design, build, and maintain scalable data pipelines, AI agents, and microservice integrations using Python, PySpark, Databricks, and agentic AI tools. Deploy and automate cloud-native, CI/CD workflows, ensure data quality and security, and drive adoption of Data Mesh/federation and AI use cases across retail and wealth risk platforms.

Design, build, and maintain scalable data pipelines and AI agents using Python, PySpark, Databricks, Kafka and microservices. Deploy and integrate agentic AI (Google ADK, Devin.AI, MCP), implement Data Mesh/federation, automate microservice integrations with cloud-native infrastructure (OpenShift/Kubernetes) and CI/CD, ensure data quality/security, and drive compliance and best practices within Retail and Wealth Risk Engineering.

Design, build, and maintain production-grade Generative AI applications and agentic workflows using LLMs, RAG, embeddings, and vector search. Architect cloud-native, scalable solutions (.NET backend, modern front-end), implement security, monitoring, cost optimization, CI/CD, and mentor junior engineers while collaborating with product and domain teams.

Design difficult open-web research problems for benchmarking frontier AI browsing agents. Responsibilities include constructing multi-step questions, validating clues through government and institutional sources, tracing evidence across archives and PDFs, documenting auditable citations, testing search difficulty, eliminating ambiguity, and delivering reproducible research documentation.

Develop, deploy, and scale end-to-end analytical solutions for the Vapor Phase Drying process. Build ML models and data pipelines, work with dashboards and model monitoring, use Python and ML libraries, leverage Azure data and ML services, apply time-series forecasting, collaborate cross-functionally, and ensure regulatory and internal compliance.

Lead the architecture and development of scalable full-stack Sales AI applications. Integrate generative AI models, retrieval systems, APIs, and agentic workflows into secure production solutions. Guide architecture across front-end, back-end, data, integrations, and cloud infrastructure while improving testing, delivery, observability, reliability, and maintainability. Lead technical decisions, investigate complex issues, mentor engineers, and collaborate with product, AI, data, security, and engineering teams.

Lead the development of AI-powered customer support solutions, guiding a team to build scalable, reliable, and secure systems using LLMs and third-party integrations.

Build and operate Five9's AI platform: provision cloud infrastructure, maintain IaC, CI/CD pipelines, model/agent runtimes, secure data/integration plumbing, enforce governance, provide observability and reliability, and enable solution builders.

The Senior AI / ML Ops Engineer will design and operate Agentic AI systems, implement MLOps practices, and ensure production automation is secure and reliable.

Own core platform features and AI-powered personalization across Binance’s main site, including homepage, user center, market pages, onboarding, recommendation strategies, and generative UI. Partner with engineering, design, data, ML, and business teams to define requirements, improve user journeys, increase conversion and engagement, establish success metrics, run experiments, and iterate on user-facing products.

Develop, deploy, and maintain scalable AI/ML pipelines and production systems, including LLMs and generative AI models. Collaborate with data scientists, software engineers, architects, and clients to integrate AI capabilities into products. Optimize model performance, latency, and cost on AWS, GCP, and Azure, using Docker, Kubernetes, CI/CD, monitoring, versioning, and other MLOps practices.

Own the architecture and delivery of production-grade generative and agentic AI systems, including LLM agents, RAG pipelines, multi-agent workflows, APIs, and cloud-native services. Establish AI engineering, evaluation, observability, guardrail, and LLMOps practices while mentoring AI/ML and backend engineers. Collaborate with Product, Data, Platform, Security, and Compliance teams to deliver scalable, secure, and business-ready solutions, and provide technical direction on GenAI strategy and architecture.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Leads customer-facing design and implementation of cloud, platform, infrastructure, data, and AI solutions. Responsibilities include cloud migration and modernization, Infrastructure as Code, CI/CD, Kubernetes, automation, GitOps, platform security, observability, and AI-assisted development. The consultant conducts workshops, creates reusable platform capabilities, enables responsible AI adoption, mentors engineers, supports proposals and pre-sales, and ensures secure, reliable, governed, and cost-effective production solutions.

Lead design and delivery of enterprise-grade AI automation: architect agentic AI and multi-agent systems, build RAG solutions, integrate AI agents with enterprise systems, define governance and HITL workflows, select foundation models, and mentor teams to deploy production-ready, secure, scalable AI automation.

Develop and maintain AI-assisted development capabilities for the Eclipse-based SmarTest semiconductor testing environment. Integrate and optimize GitHub Copilot, resolve Eclipse and integration defects, implement customer-specific features, and perform testing and quality assurance. Translate customer requirements into technical solutions, provide technical assistance, evaluate changes in AI and Eclipse technologies, and collaborate with Microsoft, Advantest teams, and external partners to improve developer productivity and user experience.

Lead post-launch operations for Sales AI applications, ensuring availability, performance, security, governance, support readiness, and continuous improvement. Establish incident, problem, change, release, and lifecycle processes; monitor service and adoption metrics; develop automation, observability, self-service capabilities, and production-readiness practices. Partner with Sales, Product, Engineering, Data, Security, and IT teams to improve user experience, platform reliability, adoption, and business value.

Design, build, and evolve secure, scalable full-stack sales applications incorporating generative AI, intelligent workflows, APIs, data services, and cloud infrastructure. Lead architecture and technical design decisions, integrate AI models and retrieval systems, improve testing, CI/CD, observability, and production reliability, and mentor engineers. Collaborate with Product, AI/ML, Data, Security, Solution Architecture, and Engineering teams to deliver enterprise applications globally.

Design, develop, train, deploy, and maintain production-grade AI/ML solutions. Build predictive, classification, recommendation, and automation systems; prepare and feature-engineer data; create scalable model-serving APIs; deploy with containers and CI/CD; monitor model performance and drift; apply Responsible AI principles; collaborate with product, engineering, and stakeholders in Agile delivery.

Design and build enterprise-scale AI search and retrieval systems using Azure AI Search and Azure OpenAI. Implement RAG/vector search, embeddings, prompt engineering, secure document-level access controls, scalable backend services, and observability to improve relevance, accuracy, and latency. Mentor engineers and drive architecture for secure, compliant enterprise RAG platforms.

Lead design, development, and deployment of production-grade LLM and RAG-based AI solutions. Improve retrieval performance, build automated evaluation and validation frameworks, instrument pgvector retrieval KPIs, integrate metrics into CI/CD, and mentor mid-level engineers while driving technical reviews and best practices.

Lead and align Engineering, ML, and Analytics to deliver scalable, reliable production AI and data systems. Own technical direction, architecture, MLOps, and platform reliability while partnering with Product, Sales, and Customers to ensure engineering work maps to measurable business outcomes.