Optum - Lead AI/ML Engineer - Generative AI Solutions

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 patterns

- MLOps 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)