Top Machine Learning Jobs in India

Lead data science initiatives using advanced statistical techniques and machine learning to produce actionable insights. Partner with cross-functional teams to design data-driven solutions, manage and ensure integrity of large datasets, develop predictive models to forecast trends, and communicate results to stakeholders while staying current on emerging technologies.

Design and lead enterprise-scale AI, machine learning, and Generative AI solutions from architecture through deployment. Build data pipelines, feature engineering frameworks, RAG applications, AI agents, and LLM-powered systems. Establish MLOps practices for versioning, experimentation, monitoring, and CI/CD. Partner with stakeholders on AI initiatives, ensure security and governance compliance, and mentor ML engineers, data scientists, and AI teams.

Build backend services, APIs, databases, and data pipelines for large-scale satellite imagery and geospatial analytics. Deploy and monitor self-hosted ML inference services in offline, air-gapped defense environments. Optimize batching, latency, throughput, GPU usage, observability, evaluations, and analyst-facing tooling while maintaining reproducible builds and secure deployment processes.

Lead product strategy and roadmap for acquiring fraud and risk monitoring. Build real-time rules, risk signals, ML and AI decisioning. Reduce fraud and chargeback losses, improve recovery and operations, run experiments, monitor key metrics, and collaborate with Engineering, Data Science, Risk, Operations and Finance.

Lead the WAAP engineering product line, define AI-era API and application protection strategy, own architecture and roadmap, hire and develop engineering teams, partner with customers, ensure reliability and incident response, and collaborate across AppSec to build AI discovery, red-teaming, and AI firewall capabilities.

The Financial Services Leader will oversee the FS Consulting Practice, managing high-level strategy, client relationships, and operational delivery across several accounts while fostering innovation and cultural transformation.

Develop and maintain scalable data pipelines using Python and PySpark. Implement ETL processes, optimize performance, and ensure data quality while collaborating with cross-functional teams.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

Design, develop, prototype and deploy complex ML/DL and image-processing algorithms for semiconductor inspection systems. Build and train models using frameworks like TensorFlow or PyTorch, write model architectures from scratch, modify loss functions, and collaborate across teams to integrate ML solutions into products.

Design, develop, and deploy production AI/ML solutions using machine learning, LLMs, RAG, AI agents, computer vision, NLP, and predictive analytics. Prepare datasets, build APIs and enterprise integrations, monitor model performance, evaluate emerging technologies, and document architectures and deployment pipelines. Collaborate with product managers, engineers, and business stakeholders to translate operational challenges into scalable AI systems across manufacturing, supply chain, recycling, and enterprise workflows.

Design and implement test plans, write test code, validate software/SDKs and AI models for geospatial AI products. Troubleshoot issues, collaborate with data scientists, create documentation, samples, tutorials, and develop pretrained geospatial AI models for imagery, 3D, and text.

Design, develop, and deploy scalable AI/ML models and intelligent agent systems. Architect LLM-powered autonomous and multi-agent workflows using frameworks such as LangChain, AutoGPT, and CrewAI. Integrate AI solutions with enterprise systems, optimize cloud deployments, and apply MLOps and CI/CD practices. Collaborate with product, engineering, and business teams while promoting responsible AI, fairness, transparency, and knowledge sharing.

Lead end-to-end development of conversational AI and LLM systems, including model architecture, fine-tuning, RAG integration, voice API integration, cloud deployment, and performance optimization. Collaborate on data preparation and training workflows, implement MLOps practices, monitor deployed models, research emerging AI technologies, and mentor junior engineers.

Provide 24x7 operational support for public/private cloud infrastructure (AWS services), monitor availability, manage incidents to meet SLAs, support production deployments, ensure fintech compliance and security best practices, and communicate technical concepts to stakeholders.

Develop and maintain Python-based optimisation models and data pipelines for energy asset revenue analysis in wholesale electricity markets. Write production-quality code, run technical testing, document designs, use AI tools to accelerate development, and collaborate on deploying machine learning components. Knowledge of energy markets and power systems is a plus.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead and architect end-to-end ML/AI solutions across multiple projects. Own model design, development, fine-tuning, deployment and integration into business applications. Optimize production ML systems for performance and scalability, mentor engineers, coordinate cross-functional teams, and ensure timely delivery of ML initiatives. Apply transformer fine-tuning and recommender/search techniques on cloud platforms.

Design, build, and scale generative AI and ML solutions including LLM fine-tuning, RAG pipelines, model training and deployment on AWS SageMaker/Bedrock, experiment tracking, and computer vision tasks. Work with vector databases, ML libraries and deep learning frameworks, and develop REST APIs and production integrations.

Lead technical vision and delivery of AI/ML and Generative AI solutions (LLMs, computer vision, forecasting). Mentor engineers, design architectures, build RAG pipelines, manage ML lifecycle, deploy and monitor models on AWS, and collaborate with stakeholders to translate business problems into scalable AI systems.

Build and operationalize on-premises large language models across multiple product use cases. Responsibilities include fine-tuning and training pipelines, embeddings and vector search, RAG architecture, model evaluation, inference serving, observability, Docker and Kubernetes deployment, knowledge graph integration, and agentic AI prototyping. The role also requires collaboration with engineering, product, data, QA, and platform teams, plus ownership of production model quality and reliability.

Design, build, and deploy AI/ML and Generative AI solutions for enterprise use cases. Build and maintain end-to-end ML pipelines, deploy models to production, and implement monitoring, observability, and model-drift detection. Work with APIs, vector databases, embeddings, and RAG-based applications, and collaborate with cross-functional teams to optimize scalable AI deployments.

The role involves shaping the technical direction of a core domain, leading architectural design, ensuring system security and scalability, and embedding AI in developer workflows while supporting various engineering teams.