Top Machine Learning Jobs in India

As a Forward Deployed ML Engineer at AION, you'll design and develop multimodal AI systems, optimize models, and engage with clients to deliver intelligent agent solutions across various applications.

The job requires designing and implementing efficient algorithms for optimizing Large Language Model inference on DNN Accelerators as part of a team of researchers and engineers.

Responsible for designing and building automated test frameworks for AI hardware/software, focusing on validation of machine learning models and CI/CD processes.

As a Senior Software Engineer, you will develop scalable AI solutions, ensure production readiness, mentor junior members, and collaborate across teams.

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

The ML QA Lead ensures quality in Cerebras software across ML workloads, focusing on testing accuracy, performance, and usability. Responsibilities include driving software quality, automating workflows, debugging issues, and collaborating across teams.

The Software Machine Learning Test Engineer will develop test automation for AI components, improve test infrastructure, and ensure software quality for machine learning models using Python and GitLab CI/CD.

Lead AI projects by translating business needs into technical solutions, developing AI applications, and integrating AI capabilities across platforms. Oversee large-scale digital transformation initiatives while mentoring teams and ensuring efficient SDLC processes.

The ML Engineer III will build, train, and optimize models, perform data processing, productionize models using Databricks, and monitor performance in AWS environments.

The role involves building and operating ML services, converting prototypes into production models, managing pipelines, and ensuring system reliability across Tekion's cloud platforms.

The Staff ML Scientist is responsible for building and optimizing ML models, implementing best practices for MLOps, and conducting exploratory data analysis using Python and its libraries.

The AI/ML Engineer will design, develop, and deploy machine learning models, utilize various learning techniques, and enhance model performance through data processing. They will work with cloud platforms and CI/CD pipelines while leveraging MLOps practices.

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 Architect - Machine Learning role involves designing cloud ML solutions on AWS, implementing Generative AI models, and collaborating with cross-functional teams to enhance LLM performance for user-friendly applications.

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 Machine Learning Engineer will design and maintain ML systems, build APIs, develop backend features, and collaborate with cross-functional teams to enhance ML workflows and monitor model performance.

Develop and optimize machine learning infrastructure and models for ad technology. Collaborate with teams for data analysis and engineering.

The Junior AI Developer will design, develop and deploy AI solutions using Python, manage multi-agent workflows, analyze data, and optimize AI models.

This role involves designing, developing, and testing software for automotive dealerships, integrating AI/ML solutions, and collaborating to build scalable technical products.

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 Lead AI/ML Engineer will design, implement, and govern enterprise AI/ML solutions while partnering with business stakeholders and ensuring compliance with governance standards.

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

The Senior Product Manager will lead ML-driven decision systems for a global Demand Side Platform, defining business objectives and owning the lifecycle of ML initiatives, collaborating with data science and engineering teams for successful deployment.

Lead a team of machine learning engineers to develop AI-driven products, focusing on deep learning and scalable system delivery while collaborating across departments.

Lead and grow a multidisciplinary ML engineering team to design, build, and deploy AI/ML solutions (LLMs, ranking, recommender systems, CV, NLP). Drive strategy, mentor engineers, manage capacity/budgeting, collaborate with product and stakeholders from prototyping to production, and communicate results to leadership.