Top Machine Learning Engineer Jobs in Mumbai

The Senior Machine Learning Engineer will develop solution architectures, build models, mentor junior engineers, and enhance client relationships while working on various ML applications.

As a ML Engineer Intern, you will build functionalities for products and integrate third-party systems, gaining hands-on experience in ML applications.

The Associate Architect - Machine Learning role involves developing cloud ML solutions on AWS, utilizing various technologies and frameworks, collaborating with teams, and designing software architecture for model deployment and optimization.

The AI/ML Architect will develop, deploy, and maintain AI and machine learning models while utilizing cloud services and big data technologies for scalable applications.

As a Staff Machine Learning Engineer, you will lead the design and deployment of machine learning systems, mentor engineers, and enhance model evaluation processes within a fintech setting.

The Senior AI/ML Engineer at Nansen will develop AI models for blockchain data analysis, collaborating with teams to enhance platform capabilities and mentor junior engineers.

This role involves designing and implementing machine learning solutions, focusing on NLP and machine vision while leveraging cloud infrastructure and MLOps practices.

Lead machine learning initiatives by designing and implementing AI solutions in NLP, machine vision, and deploying models using cloud infrastructure. Collaborate with teams to translate business needs into technical designs, ensure adherence to best practices, and conduct POCs for solution validation.

The Staff Engineer in Machine Learning will design, develop and implement AI solutions focusing on NLP, machine vision and deep learning, while collaborating with clients and developers to meet business requirements.

The Staff Machine Learning Engineer will design, deploy, and maintain scalable ML solutions, collaborate with teams, and enhance productivity through tooling.

Lead the development of Agentic AI applications, focusing on autonomous decision-making and deployment of generative AI models while collaborating across teams to enhance AI capabilities and innovations.

As a Senior ML Engineer, you will operationalize ML models, design scalable MLOps infrastructure, build feature stores, automate workflows, and ensure model monitoring, collaborating with data scientists and engineers.

The Senior Machine Learning Engineer will design, develop, and deploy ML and Generative AI solutions, ensuring high performance and reliability, while leading collaboration efforts with cross-functional teams.

The Associate Principal Engineer in Machine Learning will design AI/ML solutions, ensure architectural quality, and lead problem-solving efforts while collaborating with stakeholders.

As a Principal Engineer in Machine Learning, you will design and implement AI/ML solutions, develop technical designs, evaluate architectures, and resolve complex issues.

The AI/ML Engineer II role involves developing AI solutions, managing product backlogs, integrating LLMs, and collaborating on automation projects while leading improvements in IT processes.

The role involves designing, training, and deploying machine learning models, integrating LLMs, and implementing MLOps best practices. Collaboration with cross-functional teams is essential to drive AI initiatives.

Develop and scale data management systems for AutoML and forecasting platforms, ensuring data reliability and implementing SLAs across pipelines.

Design and develop machine learning models for banking AI use cases, translate technical requirements, and ensure best practices adherence during project implementation.

The Technical Architect for AI/ML will define architecture for AI systems, mentor teams, and align strategies with business objectives, utilizing cloud-native tools and frameworks.

The Senior Manager AI ML will define the strategic AI/ML roadmap, lead end-to-end system integration, and mentor teams to create innovative adaptive systems.

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

Lead AI/ML projects, design scalable architectures, translate business needs into technical solutions, and ensure best practices in implementation.