Lead AI / ML Engineer @ Optum

We're looking for a hands-on technical leader to design, build, and productionize AI/ML and GenAI solutions that improve healthcare operations and patient outcomes. You will own end-to-end delivery-from problem framing and data pipelines to models, MLOps/LLMOps, and ongoing monitoring-while mentoring a small team and partnering with product, data engineering, and clinical/operations stakeholders.

Primary Responsibilities:

• Lead architecture and delivery of ML/GenAI solutions (classification, forecasting, NLP, deep learning, LLM apps) at production scale
• Build robust data/feature pipelines over large clinical/claims datasets; write efficient, well-tested Python (pandas/NumPy) and SQL
• Develop and deploy LLM capabilities: prompt design, fine-tuning, RAG pipelines, vector indexing, and evaluation with guardrails
• Establish MLOps/LLMOps best practices: CI/CD, model registry, experiment tracking, monitoring, drift detection, A/B testing, cost/perf optimization
• Ensure data privacy and compliance (HIPAA/PHI handling, access controls, auditability) and champion model governance and Responsible AI
• Translate business problems into technical roadmaps; communicate trade-offs and results to executives and non-technical partners
• Mentor engineers and set engineering standards (code reviews, documentation, reliability, observability)
• Lead architecture and delivery of ML/GenAI solutions (classification, forecasting, NLP, deep learning, LLM apps) at production scale
• Build robust data/feature pipelines over large clinical/claims datasets; write efficient, well-tested Python (pandas/NumPy) and SQL
• Develop and deploy LLM capabilities: prompt design, fine-tuning, RAG pipelines, vector indexing, and evaluation with guardrails
• Establish MLOps/LLMOps best practices: CI/CD, model registry, experiment tracking, monitoring, drift detection, A/B testing, cost/perf optimization
• Ensure data privacy and compliance (HIPAA/PHI handling, access controls, auditability) and champion model governance and Responsible AI
• Translate business problems into technical roadmaps; communicate trade-offs and results to executives and non-technical partners
• Mentor engineers and set engineering standards (code reviews, documentation, reliability, observability)
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• Bachelors in Computer Science, Engineering, Math, or related field required; MS/PhD preferred or equivalent experience
• 10+ years of professional experience in software/ML engineering, including 3+ years leading projects or teams
• Hands-on GenAI experience: LLMs, embeddings, RAG, fine-tuning, evaluation; familiarity with Hugging Face and LangChain/LlamaIndex
• Solid foundation in statistics and ML (hypothesis testing, experimental design, feature engineering, supervised/unsupervised methods)
• Deep learning expertise (PyTorch or TensorFlow) and modern NLP (transformers)
• Expert Python and pandas stack; ability to write vectorized, high-performance code; solid testing practices (pytest) and Git
• Solid data engineering skills: SQL; experience with Spark/Dask and workflow orchestration (Airflow/Prefect)
• Cloud proficiency (AWS/Azure/GCP) and containerization/orchestration (Docker/Kubernetes); CI/CD and IaC (Terraform) exposure
• Proven excellent communication and stakeholder management skills

Preferred Qualifications:

• Healthcare domain experience: claims, EHR/HL7/FHIR, coding (ICD/CPT), risk adjustment, quality measures, de-identification
• Big data platforms (Databricks, Snowflake, BigQuery) and streaming (Kafka); lakehouse patternsMLOps stack: MLflow/SageMaker/Azure ML/Vertex; model monitoring/observability
• Vector databases (FAISS, Pinecone, pgvector), knowledge graphs (Neo4j), and ontologies (UMLS/SNOMED)
• Security/compliance frameworks (SOC 2, HITRUST)
• Additional languages for performance or integration (Scala/Java/Go)