Top Machine Learning Jobs in India

This role involves designing, building, and scaling a Data+ML platform, collaborating on machine learning pipelines, and incorporating cloud services for deployment and execution.

As a Senior Machine Learning Engineer, you'll design and deploy ML systems for user personalization, lead projects, optimize data pipelines, and enhance product engagement, while mentoring junior engineers.

Lead the development of advanced AI/ML solutions, mentor junior engineers, oversee model deployment, and drive innovation in AI workflows.

Develop and implement AI/ML solutions, including model development and deployment, collaborating with teams and ensuring production efficiency.

Design and maintain ML pipelines and data workflows, collaborate with data scientists, ensure code quality, support model deployment and optimization.

The Specialist Generative AI will develop and integrate generative AI solutions into enterprise applications, ensuring compliance with IT security standards and optimizing AI performance.

The Machine Learning Engineer will design and maintain ML pipelines, build AI solutions, support existing systems, and collaborate with teams to apply data and AI effectively.

As a Senior Machine Learning Engineer, you will design and build web applications and data pipelines, optimizing AI solutions for scale and efficiency.

The AI/ML Engineer is responsible for driving end-to-end machine learning projects, optimizing models for production, and ensuring compliance with company policies.

Lead AI or ML Engineer responsible for designing AI capabilities for Contact Center platform, collaborating with teams, and maintaining AI solutions.

The role involves developing machine perception solutions using AI/ML for aerospace, focusing on computer vision and mobile platforms, and collaborating with teams on innovative technologies.

Maintain and optimize CrowdStrike's ML infrastructure: diagnose distributed system incidents across Ray/Spark/SLURM/JupyterHub, profile and tune performance, build observability and runbooks, collaborate with ML teams, and mentor on debugging to ensure reliable training and inference pipelines.

Design and implement machine learning models, collaborate with teams, retrain models for efficiency, evaluate ethical adherence, and provide technical support while documenting systems.

Lead the design and delivery of enterprise AI solutions, overseeing architecture, mentoring teams, and ensuring compliance with standards and best practices.

The role involves developing and integrating ML and Generative AI solutions, collaborating with cross-functional teams, and optimizing AI pipelines at scale.

The Sr Data Engineer will develop ML solutions, lead teams, manage projects, and ensure quality while mentoring junior developers.

Lead AI/ML Engineer responsible for developing and implementing machine learning solutions, managing teams, ensuring compliance, and driving innovation in healthcare applications.

The role involves maintaining and optimizing ML infrastructure, diagnosing complex issues, performing system optimization, and collaborating with engineers to resolve workflow issues.

As an ML Engineer, you will design, experiment, and deploy AI/ML models, evaluate their performance, and leverage Python and SQL.

The Senior Director will lead K2View's global partnerships, drive pipeline growth, and design go-to-market strategies with GSIs and technology partners.

Lead and develop software engineering projects focusing on Python code, database design, and RESTful APIs while collaborating across teams to ensure quality software delivery.

Lead data science initiatives, designing cutting-edge AI solutions, mentoring teams, ensuring responsible AI practices, and advancing business objectives through data insights.

Responsible for leading a team in developing AI applications utilizing ML and NLP techniques, focusing on generative AI and data analysis solutions.

Lead development and productionization of LLM and GenAI solutions using LangChain, fine-tuning, and RAG. Write PEP8-compliant Python, containerize with Docker, ensure production-quality code and testing, apply ML/DL methods (e.g., XGBoost, K-means), and support deployments via terminal and version control.

Lead design and delivery of production-grade AI/ML solutions for healthcare, focusing on NLP/LLM, RAG, time-series forecasting, model lifecycle management, deployment, optimization (quantization), and mentorship of engineering teams.