Top Machine Learning Jobs in Delhi

The Data Operations Analyst develops custom product attribution based on client needs, utilizing tagging and machine learning; ensuring data mapping accuracy and project delivery.

The Senior Software Engineer will design and maintain backend software, APIs, and collaborate with various teams to enhance product offerings and scale services.

The Customer Success Business Partner at Lyric drives customer adoption and retention for strategic accounts, leveraging expertise in AI and supply chain operations.

WorldQuant seeks a Quantitative Researcher to develop predictive models for financial markets, emphasizing creativity and a strong quantitative background.

Lead AI initiatives, craft product vision, manage lifecycle, utilize data for decisions, and foster collaboration across teams.

Drive the Search product roadmap at Bloomreach, focusing on AI, personalization, and data-driven decisions while ensuring customer engagement and market readiness.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.

The role involves leading data science initiatives, mentoring team members, deploying models, and ensuring high-quality technical solutions. Responsibilities include coding, architecture design, and client requirement translation.

The role involves developing and implementing Machine Learning models, managing data pipelines, and supporting Data Science teams with best practices in ML operations and methodologies.

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

The AI/ML Engineer II will design, develop, and deploy scalable AI/ML solutions, optimize model performance, and integrate AI into products. The role requires collaboration with various teams and knowledge in deploying solutions to cloud environments.

The role involves collaborating with Data Science teams to implement and manage ML models, designing GenAI solutions, and automating ML operations, while establishing best practices in ML lifecycle management.

The AI/ML Engineer II is responsible for designing, developing, and deploying AI/ML solutions. They work closely with product management and engineering teams to translate business problems into scalable AI systems, focusing on model optimization, cloud deployment, and cross-functional collaboration.

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

Design and develop machine learning infrastructure and models, support product teams, build scalable data services, and promote collaboration within a diverse engineering team.

Join Weave's Machine Learning Team as a Staff ML Engineer to design and develop AI infrastructure, build scalable services, and guide teams in leveraging data for application development.

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

Design, build, and optimize ASR and speech-related ML systems, handling real-world audio for production deployment and improvements.

The role involves mentoring product teams in data and ML best practices, conducting complex data analysis, modeling, and building AI-driven workflows to automate processes and enhance product features.

The Senior Engineer will develop and deploy machine learning models and workflows, mentor junior engineers, and collaborate with cross-functional teams to improve patient care through innovative solutions.

As an AI/ML Engineer, design, develop, and deploy machine learning models and AI solutions for fintech, ensuring integration with cloud platforms and robust data pipelines.

Responsible for the end-to-end design, development, and deployment of Machine Learning models, collaborating with teams, and managing deployment lifecycles.

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

The role involves designing and deploying machine learning models, ensuring model performance, and optimizing ML infrastructure on GCP, collaborating closely with data engineers and scientists.