Top Machine Learning Jobs in India

Lead AI agent systems and LLM development, oversee technical design, engage with customers, and represent the company in AI discussions.

The Machine Learning Engineer II will develop generative AI models, evaluate multi-agent systems, and implement MLOps practices while collaborating with teams to deliver scalable solutions in cloud environments.

Design, develop, and implement ML models and algorithms, build scalable ML pipelines, and collaborate with teams for AI/ML solutions.

The AI/ML Consultant will design automation strategies for Order-to-Cash workflows, identifying improvement areas and delivering a comprehensive roadmap for implementation.

The job requires skills in AI/ML engineering including designing and implementing machine learning models. Candidates should also ensure data integrity and performance optimization.

Lead ML strategy and development, optimize models, mentor teams, and implement data-driven solutions for business growth across departments.

As a Staff Software Engineer, you'll build tooling and infrastructure for humanoid robotics, improve software quality and performance, manage MLOps, and optimize data pipelines.

Lead projects in machine learning, collaborating with engineers and data scientists, defining technical roadmaps, and implementing scalable solutions in AI technologies.

The Machine Learning Engineer is responsible for developing efficient AI models, handling data preprocessing, and collaborating with teams for model improvement and deployment.

Design, develop, and implement machine learning models and algorithms. Collaborate with teams to enhance services and deploy solutions into production.

The AI/ML Consultant will design, develop, and deploy AI/ML models, collaborate on integrations, conduct data analysis, and mentor team members.

The AI/ML Senior Manager will lead cross-functional teams, define AI/ML roadmaps, oversee solution deployment, and mentor a team of professionals, aligning AI initiatives with business goals.

The role involves simulation, modeling, performance analysis, and tooling for AI/ML workloads, focusing on hardware/software co-design in advanced computing environments.

The AI/ML Scientist will design, implement, and validate machine learning models while conducting data analysis to support strategic decisions. Responsibilities include leveraging advanced AI techniques and collaborating with teams to deliver scalable solutions that meet business objectives.

The role requires managing AI/ML project delivery, collaborating with product engineers and stakeholders, ensuring quality service delivery, and mentoring team members. Responsibilities include overseeing budget, vendor management, and driving process improvements.

Manage ML projects by collaborating with teams, overseeing AI product delivery, training, and ensuring optimal service delivery.

Lead development of AI accelerators and ML models for sustainability and safety. Engage stakeholders, mentor teams, and ensure ethical AI practices.

As an AI/ML Consultant, you will develop AI accelerators and machine learning models for sustainability, health, and safety domains, collaborating with teams to deliver innovative solutions and client engagements.

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

The Senior AI/ML Scientist will develop machine learning models, conduct data analysis, and implement AI solutions in logistics to improve supply chains.

Develop and maintain scalable APIs and Kubernetes environments for machine learning models. Ensure AI system reliability and collaborate with ML engineers to optimize deployments.

Design and develop machine learning infrastructure and models to enhance user experience. Collaborate with teams on AI integration and data lifecycle management.

Lead the development of advanced data pipelines and machine learning models to optimize online marketing campaigns while collaborating with cross-functional teams.

Analyze and optimize performance of compiler-generated code, design compiler features for high-performance architecture, and work with hardware for neural network inference.

Maximize neural network performance by enhancing a compiler, collaborating with architects, and implementing support for an advanced architecture.