Top Machine Learning Jobs in Chennai

The Senior ML Research Engineer will develop computer vision algorithms for item authentication, mentor team members, and oversee model deployment.

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.

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.

The role involves solving customer problems by building ML solutions, engaging with various stakeholders, and taking ownership of the outcome through deployment.

Designs, builds, and optimizes machine learning models and infrastructure, develops production-grade ML services and data pipelines, conducts research and evaluation, implements LLM fine-tuning and RAG solutions, and partners cross-functionally to translate business requirements into deployable ML deliverables.

Design and develop machine learning infrastructure and models, support product teams, build scalable data services, and promote collaboration within a diverse engineering team.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

As a Machine Learning Engineer, you will build and maintain ML infrastructure, develop credit models and collaborate on data-driven features. The role requires extensive hands-on experience in ML engineering within production environments.

Develop and optimize machine learning models, manage project priorities, and improve real-world solutions using advanced ML techniques.

Lead a data science team to design, build, and deploy ML models and scalable PySpark data pipelines on Azure. Oversee feature engineering, model lifecycle, monitoring, performance tuning, MLOps/CI-CD, and translate business needs into analytical solutions while presenting results to stakeholders.

Lead ML architecture and solution design for NLP, computer vision, and generative AI projects. Define technical designs, review implementations, run POCs, guide developers, and ensure scalable, secure, and ethical deployments using cloud, MLOps, and model deployment pipelines.

Lead design and delivery of ML solutions across NLP, vision, and generative AI. Convert business needs into architecture and designs, guide developers, run POCs, ensure scalability, security, MLOps, and responsible AI best practices, and build/deploy AI agents and LLM-based systems.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

Design and deliver LLM-powered applications, build retrieval pipelines, and create evaluation frameworks. Collaborate with teams on innovative AI/ML solutions.

Lead design, development, and deployment of production-ready ML/AI systems using Python, PyTorch, LLMs and related tools. Drive model research, tuning, monitoring, cross-functional integration, and mentor junior engineers while prioritizing practical business impact.

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

Design and implement end-to-end machine learning solutions: analyze use cases, build statistical and ML models, prepare and enrich data, implement scalable PySpark models in Databricks, and collaborate with product and engineering to productionize analytics for personalization and business insights.

Design, build, and maintain AI platform and ML pipelines by assembling LLMs and agentic workflows, integrating assistants with systems, prototyping in low-code and code, writing tests and CI/CD, and iterating with stakeholders on domusAI, domiAI, domusDocs and multi-agent frameworks.

Lead design, development, and production deployment of enterprise-scale ML and GenAI solutions (LLMs, RAG, recommendation systems). Architect scalable inference and training pipelines, mentor MLEs, collaborate with stakeholders, ensure security/compliance, and measure and optimize model performance and business impact.

The Senior Machine Learning Engineer will design, develop, and deploy AI systems, focusing on Generative AI and LLM integration across various business operations, ensuring high performance and scalability.

Design, train, and productionize state-of-the-art object detection systems. Build robust training pipelines, perform error analysis and ablations, optimize inference for latency and memory, and deliver production services with Docker, CI/CD, and APIs. Implement testing, monitoring, model versioning, and retraining pipelines. Rapidly prototype research ideas into deployable components and maintain clear technical documentation.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

The Senior Engineer will develop and deploy machine learning models and workflows, mentor junior engineers, and collaborate with cross-functional teams to improve patient care through innovative solutions.