Lead I - Data Science - MLOps

As a Lead ML-Ops Engineer at UST, you will be responsible for spearheading the end-to-end operationalization of machine learning and Generative AI models across platforms. Your role will involve building robust, scalable ML pipelines, embedding responsible AI governance, and integrating innovative GenAI techniques such as Retrieval-Augmented Generation (RAG) and LLM-based applications into real-world systems. Collaboration with cross-functional teams including data scientists, data engineers, product managers, and business stakeholders is crucial to ensure that AI solutions are production-ready, resilient, and aligned with strategic business goals. Your key responsibilities will include designing, implementing, and managing scalable ML pipelines using CI/CD practices, operationalizing ML and GenAI models with high availability and reliability, and automating data and model validation processes. You will also provide technical leadership and mentorship, define architecture standards, and promote engineering excellence across ML-Ops workflows. In addition, you will lead the integration of GenAI capabilities into applications, identify opportunities for business impact through cutting-edge AI technologies, and implement governance frameworks for model explainability, bias detection, reproducibility, and auditability. Strong skills in Python, Docker, Kubernetes, cloud services (AWS/GCP/Azure), CI/CD tools, ML pipeline orchestration, LLM frameworks, responsible AI, and MLOps governance best practices are essential for this role. Experience with Dataiku or similar platforms, vector databases for GenAI retrieval tasks, orchestration tools like Apache Airflow, and understanding of ML evaluation metrics in a production context are considered good-to-have skills. By joining UST, you will be at the forefront of AI innovation, work with a collaborative team, and have opportunities for career advancement in a dynamic and impactful environment.,