Top Machine Learning Jobs in Delhi

As a Principal Engineer in Machine Learning, you will design and implement AI/ML solutions, develop technical designs, evaluate architectures, and resolve complex issues.

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 designing, training, and deploying machine learning models, integrating LLMs, and implementing MLOps best practices. Collaboration with cross-functional teams is essential to drive AI initiatives.

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

Design and develop machine learning models for banking AI use cases, translate technical requirements, and ensure best practices adherence during project implementation.

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.

The Senior Manager AI ML will define the strategic AI/ML roadmap, lead end-to-end system integration, and mentor teams to create innovative adaptive systems.

Design and develop machine learning infrastructure and models to enhance user experience. Collaborate with teams on AI integration and data lifecycle management.

As a Senior Machine Learning Engineer at MediaRadar, you will design, implement, and optimize ML solutions for data products, focusing on retrieval and relevance, model evaluation, and operational excellence.

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

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.