Optum is a global health services and technology organization dedicated to helping people live healthier lives. Our team leverages cutting-edge technology to enhance health outcomes by connecting individuals with essential care, pharmacy benefits, data, and resources. We foster an inclusive culture with talented colleagues, comprehensive benefits, and opportunities for career advancement. Join us in advancing health optimization worldwide.

We are actively seeking a skilled and enthusiastic AI/ML Engineer to join our dynamic technology team. This role is central to designing and developing intelligent systems that drive our products and services. You will manage the entire lifecycle of machine learning projects, from initial data collection and preprocessing through model development, deployment, and ongoing maintenance. Your contributions will transform complex data into actionable insights and create impactful AI-driven solutions for real-world challenges.

Key responsibilities include researching, designing, and developing machine learning and deep learning models to solve business problems.

You will build and maintain robust data pipelines, ensuring data is gathered, cleaned, and transformed for model training.

This role involves training, testing, and validating models to ensure accuracy and reliability, optimizing performance through statistical analysis and experimentation.

Responsibilities extend to deploying trained models into production, implementing MLOps for monitoring, retraining, and updating models, and collaborating with data scientists, engineers, and product managers for seamless integration.

Staying current with AI and ML advancements and applying new research is crucial. You will also design, develop, and optimize LLM-powered applications and agentic workflows, including RAG, prompt engineering, and multi-step orchestration.

Ensuring LLM reliability through evaluation frameworks, guardrails, and structured output validation is also a core function.

Required qualifications include hands-on experience with ML frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn, along with integrating LLM provider APIs (e.g., Anthropic Claude, OpenAI, Azure OpenAI).

Experience in building LLM workflows and agentic applications using orchestration frameworks such as LangGraph and LangChain is essential. A deep understanding of machine learning, deep learning, and statistical modeling concepts is required.

Familiarity with cloud platforms (AWS, Google Cloud, Azure) and their ML services (Databricks), as well as core generative AI concepts (RAG, prompt engineering, embeddings, vector databases), is necessary.

Proficiency in data handling with SQL and tools like Pandas and NumPy, alongside Pydantic for data validation, is expected. Strong programming skills in Python, R, or Java are mandatory.

Excellent communication skills to articulate technical concepts, strong analytical and problem-solving abilities, and a creative, proactive mindset with a drive for continuous improvement are also essential.