Top Artificial Intelligence Jobs in Chennai

Lead technology vision and architecture for enterprise projects; design cloud-native, data-driven and Generative AI solutions; ensure scalability, security, governance, and engineering excellence; run architecture reviews, POCs, and implementations; evaluate emerging technologies; influence clients via workshops and executive engagements; mentor architects and distributed engineering teams.

Lead technology strategy and governance for multiple business units, aligning IT investments with business goals. Drive digital transformation, cloud and automation adoption, risk management, service delivery, and cost optimization. Build and mentor high-performing teams, engage C-suite stakeholders, and ensure enterprise integration and compliance to deliver measurable business value across global operations.

Design and implement Generative AI solutions to improve operational efficiency. Collaborate with teams to identify opportunities and ensure data governance.

Design and prototype technical and AI-focused online learning experiences. Partner with SMEs and vendors to produce vendor-ready curricula, modular course assets, assessments, and design systems. Drive QA, pedagogical innovation, and AI-assisted tools to measure and improve learner outcomes.

Lead development of production-ready AI/ML systems focusing on computer vision and image processing. Apply generative AI and deep learning, mentor teams, drive cross-team technical decisions, and deploy solutions on cloud or on-prem pipelines.

Leads architecture, development, deployment, and optimization of large-scale AI/ML, generative AI, and agentic systems. Oversees data pipelines, model training, monitoring, validation, and lifecycle practices while mentoring data science and engineering teams. Partners with product and business stakeholders, establishes secure and ethical AI standards, analyzes conversational data, evaluates emerging technologies, and provides technical thought leadership on scalable model architectures and MLOps.

Leads research and development of ethical, unbiased AI capabilities, including model testing, validation, privacy protection, and bias mitigation. Builds and manages a high-performing data science team, provides technical thought leadership, establishes AI governance practices, mentors talent, and partners with academia and startups. Oversees predictive, prescriptive, and adaptive models deployed in batch and streaming production environments across on-premise, cloud, and edge systems.

Lead AI and data analytics for Internal Audit: define AI strategy, build and deploy ML/NLP solutions, own data pipelines and enterprise dashboards, establish AI governance, and lead a 4–5 person technical team while partnering with global audit stakeholders to embed analytics into audit processes.

Lead integration of ethical principles into AI projects, design and deploy responsible AI solutions, oversee cross-functional teams, mentor juniors, identify ethical risks, and recommend mitigation strategies.

Own end-to-end customer implementation operations, onboarding, recurring data deliveries, production workflows, and operational support. Support AI-enabled implementations, validate data quality, troubleshoot delivery workflows, collaborate with technical and customer-facing teams, and use GenAI and automation to improve efficiency, reliability, documentation, and scalability.

Design, build, and operate production-grade backend services using Node.js and TypeScript. Integrate LLMs and AI services, develop RESTful APIs and data layers, implement RAG pipelines with vector databases, and apply agentic workflows. Maintain automated testing with Jest and Playwright, deploy services on Azure or AWS, and support scalable systems for banking and fintech clients with strong reliability, security, and auditability requirements.

Lead HR analytics, reporting, and automation projects: build and automate HR dashboards, support HRIS administration and module deployments, collaborate with AI Transformation to design automation/AI solutions, analyse people data and present insights to stakeholders, and follow company security policies.

Design, develop, deploy, and scale enterprise AI and machine learning solutions, including predictive models, recommendation engines, automation, and model-serving APIs. Build data pipelines and MLOps workflows, monitor model performance and drift, troubleshoot production systems, and implement responsible AI practices. Collaborate with technical and business stakeholders, contribute to architecture and code reviews, and mentor junior engineers while applying cloud-native technologies.

Build, deploy, and manage end-to-end AI services for traditional and generative AI use cases. Design scalable AI/ML solutions, distributed computing systems, training and deployment pipelines, and advanced algorithms. Analyze and optimize model performance across cloud platforms, GPUs, containers, and serving platforms while collaborating with cross-functional teams to deliver business-focused solutions.

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.

Lead AI opportunity discovery, prioritize use cases, design AI-enabled product strategies and implementation roadmaps. Translate operational challenges into feasible AI solutions, assess data readiness, define high-level architectures, build ROI models, develop governance for responsible AI, and drive stakeholder engagement and field-based implementation (50–80% travel).

Regional lead for AI upskilling and enablement in DTCC India. Drive AI awareness, design and deliver learning programs, build AI communities, partner with business and L&D for change management, identify and accelerate use cases, and measure adoption and business impact.

Build, deploy, and integrate LLM-based and AI tooling into developer workflows and CI/CD pipelines; troubleshoot and support developers; embed AI in build/upgrade/security processes; measure outcomes and iterate on tooling.

Develop and deploy production-ready AI applications using LLMs, generative AI, cloud AI services, and on-premises pipelines. Design scalable AI systems involving deep learning, neural networks, chatbots, and image processing. Lead implementation, make technical decisions, coordinate cross-functional teams, optimize model deployment, and mentor junior engineers. The role requires at least five years of LLM experience and 15 years of full-time education.

Lead design and delivery of large-scale AI/ML systems, microservices, and data engineering pipelines. Build and deploy LLM-based solutions using modern ML frameworks, cloud-native architectures, CI/CD, and observability while managing stakeholders and enterprise agile delivery in financial services.

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.

Design and deliver production-ready AI/ML applications and pipelines using cloud or on‑prem platforms. Lead architecture decisions, apply generative AI, deep learning, and image processing, mentor teams, and drive cross-team AI adoption.

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.

Own the cross-domain data and AI product roadmap for a reference and master data platform. Lead source and market onboarding, data-quality standards, reconciliation, AI enrichment and matching use cases, backlog prioritization, user stories, acceptance criteria, testing, SQL validation, UAT, governance, and rollout decisions. Align Process Owners, Solution Owners, Program Managers, Data Science, and Engineering while measuring data coverage, quality, and AI adoption.

Leads customer engagement and AI domain architecture for a bank, defining target-state architectures, AI-first roadmaps, governance frameworks, and enterprise technology strategies. Designs secure, scalable CRM, digital, data, workflow, and core-platform solutions using Dynamics 365, Power Platform, cloud, microservices, and AI. Provides design authority, oversees architecture reviews, drives modernization and automation, influences senior stakeholders, develops investment cases, and promotes DevOps and architectural excellence.