Join Optum, a global leader in healthcare technology, and contribute to improving health outcomes for millions. We are seeking a skilled Architect to design, develop, and deploy advanced AI solutions. At Optum, you will be part of an inclusive culture with opportunities for professional growth and comprehensive benefits. Help us advance health optimization globally by connecting people with the care and resources they need.

Our mission at UnitedHealth Group is to foster healthier lives and enhance the healthcare system for everyone. We are dedicated to addressing health disparities and enabling equitable care.

Design, build, and maintain scalable machine learning and Generative AI solutions for clinical, pharmacy, and operational use cases. Develop predictive models, recommendation systems, NLP, deep learning models, and LLM applications. Construct and optimize data pipelines, feature engineering, and model training frameworks. Implement RAG architectures, vector search, prompt engineering, and LLM evaluation frameworks. Fine-tune and deploy foundation models, ensuring reliability and performance. Apply MLOps and LLMOps best practices for CI/CD, testing, deployment, and monitoring. Collaborate with cross-functional teams to translate requirements into AI solutions. Adhere to Responsible AI principles, data governance, HIPAA, and security standards. Analyze model performance and optimize for accuracy, latency, cost, and user experience. Provide technical leadership, mentor junior engineers, and conduct code reviews. Evaluate emerging AI technologies and propose innovative solutions. Support production AI systems through monitoring and root cause analysis.

A Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field is required. Possess 5+ years of professional experience in software engineering, machine learning engineering, or AI development. Demonstrate 3+ years of experience deploying machine learning solutions in production. Proven hands-on experience with Generative AI technologies like LLMs, embeddings, prompt engineering, RAG, and vector databases. Experience developing cloud-native solutions on AWS, Azure, or GCP. Familiarity with containerization and orchestration tools such as Docker and Kubernetes. Solid understanding of MLOps practices, including model deployment and monitoring. Strong foundation in machine learning fundamentals, statistical modeling, and model evaluation. Proficiency in Python and ML frameworks like PyTorch, TensorFlow, or Scikit-Learn. Expertise in SQL, data modeling, and large-scale data processing. Ability to communicate complex technical concepts effectively to diverse audiences.