Top Machine Learning Jobs in Pune

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

As a Senior ML Engineer, you'll design production-grade systems for LLM benchmarking, develop LLM Proxy functionality, implement monitoring, and lead automation efforts, collaborating with cross-functional teams to enhance AI-driven experiences at Zendesk.

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

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.

Design and implement scalable machine learning models, automate tests, and utilize data engineering for actionable insights. Collaborate on AI/ML solutions meeting business needs.

Assist in building scalable data workflows, support AI/ML projects, and contribute to mentor-led development and research on GCP features.

The Senior Machine Learning Engineer will design and maintain ML infrastructure, develop evaluation frameworks for LLMs, ensure system reliability, and mentor team members.

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.

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 and implement AI/ML solutions for workforce management, focusing on forecasting, scheduling, and LLM integration, while collaborating cross-functionally.

The role involves designing, building, and scaling ML/RL systems that enhance customer experience while ensuring reliability and efficiency in production. Responsibilities include model optimization, deployment, and monitoring for performance improvements.

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

Design, develop, and deploy machine learning models and AI solutions. Collaborate with teams for intelligent automation and conduct research on new AI technologies.

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.

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.

Design, develop, and implement ML models for search and recommendation systems. Collaborate with data engineers and scientists to integrate, enhance and scale ML solutions for personalization in various sectors.

Design, develop, and deploy production ML models; build feature pipelines; run experiments and A/B tests; monitor and maintain models; collaborate with engineers, product, and executives; mentor junior engineers and document models and processes.

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.

As a Senior AI Engineer, you will design and implement scalable AI systems, mentor junior engineers, and ensure impactful, ethical AI solutions. Responsibilities include collaborating with stakeholders and fine-tuning generative AI models.

Design, build, and productionize scalable AI/ML pipelines and models (including LLMs and Generative AI). Deploy and optimize models across cloud and edge, implement MLOps (CI/CD, versioning, monitoring), and collaborate with data scientists and engineering teams to deliver reliable, low-latency AI solutions.

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

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.