Top Machine Learning Jobs in Chennai

Lead strategy and development of AI/ML engineering solutions, enhancing decision-making and automation across the enterprise. Oversee advanced analytics and mentoring of data science teams.

Design, develop, and deploy enterprise AI/ML solutions including GenAI, LLM-based RAG systems, and agentic AI. Build scalable models, integrations, monitoring, and reusable frameworks while collaborating with stakeholders for production adoption and continuous improvement.

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.

Build and maintain Python backend services and internal tools for AI/LLM applications. Integrate LLM APIs, design scalable REST APIs and async workflows, handle prompt management and model routing, monitor performance, debug distributed systems, automate data workflows, and collaborate to productionize AI features with emphasis on scalability, reliability, and safety.

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

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Design and implement AI models, operationalize ML models, develop CI/CD pipelines for ML workflows, and provide technical guidance while ensuring data security compliance.

Design, develop, and deploy scalable AI/ML and Generative AI solutions (LLMs, RAG, agentic AI). Build AI agents, create intelligent workflows, fine-tune and monitor models, integrate with enterprise systems, and support production deployment and continuous improvement.

Create and review realistic, locale-specific robotics and natural sciences tasks in Marathi; apply scoring rubrics; review and score AI and human responses; produce gold-standard solutions and expert feedback.

As an AI Data Expert, you'll evaluate AI-generated outputs, review text, analyze multimedia, and provide structured feedback based on cultural standards.

Create, adapt, and score domain-specific robotics tasks and rubrics in Telugu to evaluate AI systems; review submissions for accuracy, regulatory alignment, and produce gold-standard solutions and expert feedback.

Create and review realistic, domain-specific robotics tasks in Hindi; apply scoring rubrics; evaluate AI and human responses for accuracy, regulatory alignment, and professional quality; and produce gold-standard solutions and expert feedback.

Create and review realistic, locale-specific robotics and natural sciences tasks in Tamil to evaluate AI systems; apply scoring rubrics, review and score submissions for accuracy and regulatory alignment, and produce gold-standard solutions and expert feedback.

Senior Databricks MLOps Engineer responsible for automating the end-to-end ML lifecycle: CI/CD, packaging, deployment, scheduling, monitoring, and reproducible experiments. Build frameworks and pipelines, integrate embedding/LLM components (RAG, vector DBs, LangChain), optimize ML workloads, and collaborate with data scientists and platform teams to productionize models for Claims Payment Integrity.

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

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

Lead design and production of computer vision biometric systems (face detection, quality, attributes, recognition). Build/train models, implement fairness benchmarking, own end-to-end ML pipelines on AWS, optimize for low-latency inference, and mentor engineers.

Design, develop, and deploy production-grade ML solutions and Digital Twin models for smart manufacturing. Build predictive/prescriptive models (predictive maintenance, anomaly detection, throughput, scheduling), create feature and streaming pipelines, implement MLOps (training/deployment, model registry, drift monitoring), collaborate with engineers and SMEs, support plant visits, and ensure scalable, monitored AI solutions for industrial operations.

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

Build and operate MLOps platforms on AWS for autonomous-driving ML workloads. Maintain highly available multi-zone training and deployment environments, run distributed multi-GPU training (e.g., Ray), implement Airflow orchestration and MLflow model lifecycle, support GitHub-based CI/CD for code/models/infrastructure, and ensure reproducible, traceable, auditable ML workflows.

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.

Lead design and production of face recognition and biometric CV systems. Build, train, optimize models (PyTorch/TensorFlow/JAX), own end-to-end ML pipelines (Airflow), ensure fairness, deploy low-latency inference on AWS, and mentor engineers.

Design, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

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.

Design, implement, and lead large-scale generative AI and ML systems (LLM fine-tuning, RAG, agents, Graph ML). Build REST APIs, deploy with Docker/Kubernetes, optimize big-data workflows, mentor engineers, and operate ML services on cloud platforms.