Top Machine Learning Engineer Jobs in Mumbai

As a Software Engineer, you'll enhance the user experience of ML tools, optimize workflows, and collaborate with product managers on ML infrastructure projects.

The Senior Machine Learning Engineer will work on the Predictions workspace, developing predictive models for supply chain challenges and ensuring seamless user experiences through orchestrated workflows.

As a Machine Learning Engineer II, collaborate with ML scientists to deploy models, build infrastructure, automate workflows, and define scaling vision for ML applications.

Build, train, and refine ML models using real human behavior data. Develop data pipelines, own model deployment, and integrate models into apps.

As an Engineer I, you will develop high-quality software by gathering requirements, writing code, performing impact analysis, and managing testing phases.

The Associate Architect will lead the development and deployment of machine learning solutions on Azure, manage the ML lifecycle, and optimize existing models while providing technical leadership to project teams.

The ML Product Owner will shape the supply chain platform by developing forecasting capabilities, collaborating with teams to enhance decision-making using machine learning.

You will design and develop machine learning infrastructure and models, work with cross-functional teams, and build AI-driven applications, focusing on data scalability and performance.

Develop and deploy AI and ML models, collaborate with teams to design algorithms, and leverage data for insights and solutions.

Lead the end-to-end data science pipeline, develop AI/ML models, collaborate with engineering teams, and engage business users to translate complex problems into solutions.

As a Machine Learning Engineer, you will develop AI solutions, own AI features, integrate ML into CI/CD, mentor peers, and work on infrastructure challenges.

Seeking an AI/ML Engineer with 4+ years of experience to design, develop, and implement AI models and solutions, collaborating across teams to translate enterprise needs into practical applications.

Develop and optimize machine learning models, manage project priorities, and improve real-world solutions using advanced ML techniques.

The Principal Engineer, Machine Learning will design AI solutions for telecom use cases, collaborate with teams, oversee deployment, and ensure compliance with data regulations.

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

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 AI/ML Engineer will develop and optimize AI-driven media search, fine-tune models, handle large datasets, and work with engineering to deploy scalable AI endpoints.

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.

Design, develop, and scale ML systems processing telemetry data. Lead cross-functional teams, mentor engineers, and innovate in AI-driven solutions.

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 and implement advanced ML algorithms for search and recommendations, collaborating with data teams and staying updated on ML advancements.

The AWS Solutions Architect- AI/ML will design and implement cloud solutions, focusing on modernization, data engineering, and AI/ML integration to support client goals.

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

Lead the design, development, and delivery of advanced machine learning models and systems, particularly in NLP and multi-agent systems. Manage a team of engineers and scientists while collaborating cross-functionally to align ML efforts with business goals.