Top Machine Learning Jobs in India

Design, build, and scale Azure ML solutions and data pipelines for LLMs, RAG, and agentic AI systems. Implement MLOps/DataOps practices, run A/B tests, monitor production models, and collaborate with stakeholders to deliver AI-driven decision support.

Build and optimize Angular front-end features that integrate AI/ML models and APIs. Collaborate with data scientists, backend engineers, and designers to implement responsive, secure UI components, visualize model outputs, and deploy AI-powered web functionality.

Build, test, and document data science, ML, analytics, and GenAI solution components. Prepare and validate data, develop features, prototypes, prompts, retrieval, and evaluation datasets. Execute evaluation plans, troubleshoot defects, maintain technical artifacts, follow governance and Responsible AI practices, and contribute reusable code and documentation while collaborating with global teams.

Design, train, evaluate, and deploy predictive transportation and delivery ML models (primarily XGBoost). Perform EDA, feature engineering, experiment design, and statistical evaluation. Build production ML pipelines and MLOps practices on Google Vertex AI. Collaborate with data engineering and supply chain stakeholders to translate operational needs into scalable solutions.

Design and build Python-based integrations, data pipelines, APIs, and AI-enabled workflows; deploy and maintain LLM/NLP/RAG capabilities in cloud environments; troubleshoot production issues; lead customer-facing implementations and mentor engineers to deliver scalable, reliable solutions.

Lead design and delivery of simulation, optimization, and AI/ML solutions for container terminal operations. Build operational and strategic tools, coach junior team members, liaise with stakeholders, measure model impact, and iterate models to improve efficiency in yard, vessel, and truck operations.

Design and develop end-to-end ML solutions, build and productionize models and data pipelines, work with offshore teams, mentor junior engineers, engage clients, and identify sales opportunities.

Develop and support end-to-end AI/ML and GenAI pipelines for transformer design, operations, manufacturing, and service. Architect cloud-based AI resources, build LLM/RAG assistants and agentic workflows, implement MLOps/GenAIOps practices, collaborate with engineers and stakeholders, and document and validate scalable AI solutions.

Lead design, build, and operationalize scalable machine learning and generative AI solutions. Own projects end-to-end, collaborate with stakeholders, mentor engineers, and contribute to core ML platforms, data pipelines, model deployment, and architecture to deliver business impact.

Design, build, deploy, and monitor ML models at scale. Collaborate with DS, QA, infra, and engineering teams to productionize and maintain ML services, optimize production code, and evolve solutions using modern ML practices.

Develop methodology, flows, and tools for SoC and IP development efficiency; work with teams on automation tools using VLSI expertise.

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.

Lead design, development, and deployment of AI/ML solutions for sustainability, ESG, and H&S. Hands-on agent and LLM/BERT development, architect enterprise-grade systems, manage end-to-end ML lifecycle (data strategy, MLOps, production), advise senior clients, mentor teams, and ensure responsible AI, governance, and scalability.

The AI/ML Engineer will build scalable data-intensive AI/ML solutions, develop pipelines, optimize data processing systems, and manage end-to-end AI/ML project lifecycles.

Lead design and delivery of scalable GenAI and MLOps pipelines, automate model lifecycle (CI/CD, versioning, monitoring, governance), integrate LLMs into business workflows, optimize hybrid/multi-cloud AI infrastructure, run PoCs on emerging tools, mentor teams, and drive enterprise-grade productionization of AI solutions across finance, supply chain, manufacturing, and other domains.

The Data Scientist will develop and deploy AI-powered predictive models for MRI service operations, enhancing system uptime and healthcare outcomes through collaboration with various teams and stakeholders.

The Machine Learning Engineer will manage the full lifecycle of model deployment, enhance model accuracy and performance, and design data pipelines for voice AI applications.

As an MLOps Engineer, you'll enhance R&D infrastructure for ML solutions, ensuring reliability and scalability, while collaborating with diverse teams.

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.

The AI/ML Architect designs, builds, and deploys scalable AI/ML solutions, collaborates with teams, and writes production-grade code.

Seeking an AI/ML Engineer to design, develop, and deploy AI and machine learning solutions, using state-of-the-art models and technologies.

As a Machine Learning Engineer at Level AI, you'll work on NLP challenges, develop scalable solutions, collaborate with teams, and innovate using cutting-edge ML techniques.

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, build, and deploy scalable AI/ML solutions and pipelines (training, validation, deployment, monitoring). Collaborate with product and engineering to turn business problems into models (NLP, recommendations, anomaly detection). Implement MLOps, CI/CD, and production infrastructure for batch and real-time inference while maintaining model governance and performance.

The AI/ML Engineer will analyze diverse data sources, create machine learning models, and communicate insights to clients, ensuring effective data-driven decision-making.