Top Machine Learning Jobs in India

Design, build, and maintain data pipelines and knowledge stores (vector and graph) for agentic and generative AI. Implement RAG/RAQ workflows, embedding generation, ingestion of structured/unstructured data, monitoring, governance, and feedback loops. Collaborate with AI/ML, MLOps, Data, and Product teams to ensure scalable, auditable, and high-fidelity data infrastructure.

As an ML Ops Engineer, you will focus on deploying ML services, managing cloud infrastructure, orchestrating ML pipelines, and supporting AI models in production.

As a Staff Machine Learning Engineer, you'll architect and maintain backend services, integrate ML models, and collaborate across teams to ensure efficient project execution.

Design and develop end-to-end AI capabilities in NLP, optimize ML models and systems, and collaborate with cross-functional teams.

Design and develop AI and ML models for payment systems, with a focus on fraud detection, risk mitigation, and collaboration with global teams.

Design and develop AI/ML models for transaction monitoring and fraud detection, leading research, deploying models, and collaborating in cross-functional teams.

The Senior Machine Learning Engineer will develop and implement machine learning algorithms, enhance LLM integration, analyze data for insights, and contribute to open-source projects.

Design, build, and own production data pipelines and ML systems to consume GIS datasets, productionise models on AWS, deploy monitoring and DQ automation, mentor teammates, communicate with non-experts, and deliver robust scalable analytics from prototype to maintenance.

Lead design, development, and deployment of agentic multi-agent AI systems using LangGraph and LLM orchestration. Implement RAG, vector DBs, knowledge graphs, and RLHF/RLAIF to build scalable, memory-augmented, self-improving agents; mentor teams and translate PoCs into production.

As a Machine Learning Engineer, you will solve business problems using machine learning and AI, develop POCs, and support team growth through collaboration and knowledge sharing.

Lead design and implementation of advanced AI systems for supply chain platforms, focusing on multi-agent coordination and automation while ensuring reliability and scalability.

The job involves building tools for machine learning, collaborating on productionizing models, and maintaining existing platforms with newer tech solutions.

The role involves designing and implementing AI/ML models for edge computing environments, collaborating across teams, and improving software solutions while staying current with technology advancements.

The role involves managing AI and ML projects, leveraging data analytics to provide services to diverse clients, and ensuring performance-based outcomes.

Lead the Agentic AI team to build AI-powered solutions for AIOps and DDI, focusing on innovative applications using LLMs and advanced AI/ML techniques.

The ML Product Owner will shape the supply chain platform by developing forecasting capabilities, collaborating with teams to enhance decision-making using machine learning.

Design, build, and deploy scalable ML models and data pipelines for prediction, anomaly detection, personalization, and automation. Operationalize models at cloud scale, implement observability and drift detection, run experiments/A-B tests, and integrate ML solutions into core platform services. Drive performance, reliability, and cost-efficiency across model training and inference.

Lead design and scaling of AI solutions, manage ML pipelines, ensure compliance, and collaborate with global teams for implementation and optimization of advanced AI systems.

The role involves building and deploying services on Azure/AWS, automating Azure services, implementing cost optimization, and creating dashboards for financial analytics.

As a hands-on GenAI Data Engineering leader, you will architect and build production-grade GenAI solutions, overseeing data pipelines, governance, and operational efficiency while partnering with various teams to ensure responsible AI practices.

The AWS Architect is responsible for overseeing project development, managing technical support, establishing DevOps best practices, and leading teams while ensuring cloud governance and integration of services.

Design and develop APIs using Python frameworks, implement Generative AI technologies, deploy Docker applications with Kubernetes, and manage CI/CD pipelines. Collaborate with cross-functional teams while maintaining security best practices.

The role involves designing and developing APIs with Python, implementing Generative AI features, managing deployments, utilizing Azure services, and optimizing CI/CD pipelines, with an emphasis on security and collaboration.

Design and build AI applications, deploy ML models, analyze datasets, and collaborate with teams to enhance performance. Requires strong experience in secure application development and various ML techniques.

The Senior AI/ML Engineer will lead the development of enterprise AI solutions, owning architecture and delivery, mentoring engineers, and ensuring compliance with responsible AI practices.