We are seeking a highly motivated Machine Learning Engineer to join our Data Science team. In this role, you will not only design, develop, and deploy machine learning models at scale but also play a key role in shaping next-generation AI/ML and GenAI enabled products that will help our suppliers grow their business. You will collaborate with data scientists, software engineers, and product stakeholders to ensure ML models and AI agents seamlessly integrate into production systems, driving measurable business impact. What you'll do... Infrastructure & MLOps Build and maintain end-to-end ML infrastructure for data ingestion, feature engineering, training, deployment, and monitoring. Develop tools and frameworks that streamline experimentation for data scientists. Ensure best practices in MLOps: CI/CD for ML, automated retraining, model versioning, and monitoring. Optimize system performance, reliability, and cost efficiency in production environments. Collaboration & Leadership Partner with data scientists to move research prototypes into robust, scalable systems. Collaborate with platform and infrastructure engineers to optimize compute, storage, and deployment efficiency. Advocate for ML engineering best practices and scalable design patterns. Mentor junior engineers and data scientists on production readiness and infrastructure use. ML Systems/Recommendation Systems Enable design, train, and responsible for deploying ML models or Recommendation System (ranking, retrieval, embeddings, personalization). Collaborate with data scientists to bring prototypes into production. Agentic AI Enablement Integrate recommendation outputs into a centralized agentic AI system to enhance reasoning and decision-making. Support workflows such as planning, contextual decision-making, and multi-agent coordination.