Join Optum, a global leader dedicated to improving health outcomes through innovative technology. Our mission is to empower individuals to lead healthier lives by connecting them with essential care, pharmacy benefits, data, and resources. At Optum, you'll discover an inclusive culture, collaborate with exceptional peers, and benefit from comprehensive development opportunities.

Advance health optimization on a global scale and make a tangible impact on the communities we serve. Embrace our values of Caring, Connecting, and Growing together as you contribute to our vital work.

Key responsibilities include designing and developing scalable data pipelines using Databricks (Spark/PySpark), orchestrating workflows with Apache Airflow, and building/optimizing ELT pipelines in Snowflake. You will develop production-quality Python code for data transformations and AI/ML workloads, and design, build, and host APIs, AI services, and data products using modern Python frameworks.

Further duties involve deploying and managing APIs and ML models with Azure-native services, designing and implementing AI/ML and Generative AI solutions (including prompt engineering and RAG), and building vector databases and semantic search capabilities. You will also develop and operationalize ML pipelines, leverage LLMs for automation, and apply Azure cloud best practices for security, scalability, and governance. Implementing data quality checks, lineage tracking, and collaborating with cross-functional teams to build AI-powered products are also core aspects of this role.

Required qualifications include a graduate degree or equivalent experience, with solid hands-on expertise in Databricks (Spark/PySpark) within production environments. Proven experience in building and hosting REST APIs, AI services, and microservices using Python frameworks is essential.

Candidates must possess experience with Azure AI Services, Azure OpenAI, or equivalent AI platforms, and familiarity with Large Language Models (LLMs), Generative AI, RAG architectures, vector databases, and prompt engineering. Experience with AI governance, model evaluation, privacy, security, and responsible AI practices is crucial. Proficiency in CI/CD, Git, Infrastructure as Code, and deployment automation, along with solid working experience in Snowflake and Microsoft Azure, are also required. Knowledge of Agentic AI frameworks is a plus.