AI or ML Engineer

About the Role

The AI/ML Engineer will be at the heart of designing and building intelligent systems that power Optum's products and services. This role involves working on the complete lifecycle of machine learning projects, from data collection and preprocessing to model development, deployment, and maintenance. The engineer will transform complex data into actionable insights and create AI-driven solutions.

Key Responsibilities

• Design and develop machine learning and deep learning models to address business challenges.
• Build and maintain robust data pipelines, including gathering, cleaning, and transforming data.
• Train, test, and validate machine learning models for accuracy and reliability.
• Deploy trained models into production environments and maintain their lifecycle, implementing MLOps best practices.
• Collaborate with data scientists, software engineers, and product managers to integrate AI/ML models.
• Stay up-to-date with advancements in AI, machine learning, and deep learning.
• Build Generative AI & LLM workflows, including retrieval-augmented generation (RAG), prompt engineering, tool/function calling, and multi-step orchestration.
• Ensure LLM reliability and evaluation through frameworks, guardrails, and structured output validation.

Requirements & Tech Stack

• Hands-on experience with ML frameworks and libraries: TensorFlow, PyTorch, Keras, Scikit-learn.
• Hands-on experience integrating LLM provider APIs (e.g., Anthropic Claude, OpenAI, Azure OpenAI).
• Experience building LLM workflows and agentic applications using orchestration frameworks such as LangGraph and LangChain.
• Deep understanding of machine learning, deep learning, and statistical modeling concepts.
• Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and their machine learning services (Databricks).
• Familiarity with core generative AI concepts: retrieval-augmented generation (RAG), prompt engineering, embeddings, and vector databases (e.g., Pinecone, FAISS, Chroma).
• Proficiency in data handling, including SQL, and experience with data processing tools like Pandas and NumPy.
• Proficiency with Pydantic for data validation and enforcing structured, type-safe outputs from LLMs.
• Proven excellent communication skills.
• Demonstrated solid programming proficiency in languages like Python, R, or Java.
• Demonstrated solid analytical and problem-solving abilities.
• Demonstrated creative and proactive mindset with a drive for continuous improvement.

Qualifications

• Experience with containerization technologies like Docker and Kubernetes.
• Experience with LLM observability and evaluation tooling, fine-tuning, or prompt-optimization techniques.
• Experience in Databricks.
• Proven experience deploying and managing machine learning models in a production environment.
• Knowledge of MLOps principles and tools.
• Contributions to open-source projects or a solid portfolio of relevant personal projects.