Top Machine Learning Jobs in Chennai

As an AI/ML Engineer, design, develop, and deploy machine learning models and AI solutions for fintech, ensuring integration with cloud platforms and robust data pipelines.

Build and operate production ML/LLM systems: design MLOps platform capabilities, standardized pipelines, model registry and artifact lineage; deploy using containers/Kubernetes and CI/CD; implement observability, SLOs, incident response, model/data monitoring, security controls, and enable teams with documentation and best practices.

Responsible for the end-to-end design, development, and deployment of Machine Learning models, collaborating with teams, and managing deployment lifecycles.

Develop and scale data management systems for AutoML and forecasting platforms, ensuring data reliability and implementing SLAs across pipelines.

The role involves designing and deploying machine learning models, ensuring model performance, and optimizing ML infrastructure on GCP, collaborating closely with data engineers and scientists.

Design, implement, and maintain machine learning systems. Collaborate with data experts and transform business problems into data problems through statistical modeling and analysis.

The Director of AI/ML will lead the creation of a new Agentic Delivery Center in India, transforming client needs into scalable AI/ML solutions while ensuring project success and value delivery. Responsibilities include collaborating with leadership, defining architectural standards, and mentoring teams._

Design, develop, and implement ML models for search and recommendation systems. Collaborate with data engineers and scientists to integrate, enhance and scale ML solutions for personalization in various sectors.

Lead AI/ML projects, design scalable architectures, translate business needs into technical solutions, and ensure best practices in implementation.

Design, build, and productionize scalable AI/ML pipelines and models (including LLMs and Generative AI). Deploy and optimize models across cloud and edge, implement MLOps (CI/CD, versioning, monitoring), and collaborate with data scientists and engineering teams to deliver reliable, low-latency AI solutions.

The role involves designing and optimizing large-scale ML data pipelines, ensuring high-throughput data ingestion and processing, collaborating with teams on data workflows, and architecting GPU-based environments.