Top Machine Learning Engineer Jobs in Mumbai

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.

The Senior Machine Learning Engineer will develop and optimize multiagent frameworks, manage ML pipelines, and enhance conversational AI systems, ensuring quality in production ML environments.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

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.

As an AI/ML Engineer, you will develop machine learning models, collaborate with teams, preprocess data, and optimize ML pipelines. Additionally, you will mentor junior members and contribute to AI/ML strategies.

As an ML Ops Engineer, you will focus on deploying ML services, managing cloud infrastructure, orchestrating ML pipelines, and supporting AI models in production.

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

The role involves managing AI and ML projects, leveraging data analytics to provide services to diverse clients, and ensuring performance-based outcomes.

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.

Lead the development of machine learning products by driving technical direction, designing data pipelines, and collaborating with teams to innovate solutions in consumer finance.

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.

The role involves developing and managing AI/ML backend systems, optimizing performance for large-scale products, and collaborating with applied ML teams.

The Staff Engineer will design and build backend services for AI/ML solutions, focusing on performance, reliability, and collaboration with ML teams. Responsibilities include implementing retrieval systems, optimizing ML workflows, and ensuring observability.

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

Lead design and scaling of AI solutions, manage ML pipelines, ensure compliance, and collaborate with global teams for implementation and optimization of advanced AI systems.

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.