Optum is improving healthcare through innovative technology and data solutions. They are seeking a Lead Software Engineer to design and implement AI systems and machine learning solutions that enhance healthcare delivery and operations, ensuring compliance in a regulated environment.
Responsibilities:
- Design and implement (multi) agentic workflows where LLMs plan, decompose tasks, invoke tools/APIs, and synthesize answers across heterogeneous data sources and services
- Build retrieval augmented generation (RAG) and hybrid search pipelines to power robust question answering over clinical and operational data
- Design, code, test, document, and maintain high quality, scalable Big Data and cloud solutions
- Develop scalable microservices and APIs for integrating agent capabilities into clinician tools and internal apps
- Create prototypes/POCs and conduct design/code reviews to derisk delivery and raise engineering quality
- Leverage and adapt LLMs; perform prompt engineering, grounding, guard railing, and domain adaptation for healthcare terminology and tasks
- Design intelligent frameworks and finetune models for compliance, accuracy, and ethical standards
- Establish evaluation frameworks (automatic + human in the loop) to measure faithfulness, helpfulness, bias, toxicity, privacy leakage, and overall quality
- Partner with data engineering to build feature/retrieval stores, embeddings pipelines, and ETL/ELT jobs on Spark/Databricks; design analytics models and rules engines
- Define and develop APIs for integrations across the enterprise; improve data access patterns for low latency inference
- Own MLOps/LLMOps: CI/CD for models/prompts, automated tests (unit/contract/eval), versioning, lineage, rollback; enable blue/green or canary releases
- Instrument SLOs/SLIs (latency, availability, hallucination/defect rate) and cost KPIs (tokens, GPU hours) with dashboards and alerts
- Lead production deployments on internal platforms (e.g., UAIS) with solid observability, reliability, and cost controls
- Champion HIPAA and regulated industry controls; integrate access controls, PHI/PPI safeguards, data minimization, encryption, and auditability
- Collaborate with legal, compliance, and clinical safety to operationalize Responsible AI principles
- Analyze and define customer requirements; assist in defining product technical architecture and delivery roadmaps
- Provide effort estimates and inputs for resource planning; collaborate with QA, architecture, and peer teams
- Write technical documentation, support production, and mentor engineers and data scientists; keep skills current through continuous learning
Requirements:
- Bachelor's in Engineering, Computer Science, IT, or related field
- 12+ years of total IT experience
- 8+ years of hands on software development/data engineering/analytics with strong AI/ML delivery (Azure preferred) with Scala, Python, PySpark
- 4+ years of hands on experience with Databricks
- 4+ years of experience with ADF/Airflow (orchestration/scaling)
- 4+ years of experience with bigdata and streaming (Hadoop, MapReduce/HDFS, Spark, Kafka); Docker/Kubernetes
- 4+ years of experience with MySQL and NoSQL databases
- 4+ years of experience with Agile/Scrum, GitHub, Jenkins CI/CD, JUnit; strong coding standards and code reviews
- 2+ years of experience with LLMs and GenAI (Langchain, LangGraph, RAG, Vector DB, Azure Open AI, MCP Server, Agents, LangFuse)
- 2+ years of experience with container (Docker/Kubernetes)
- 1+ years with Proficiency building services or full stack apps (e.g., FastAPI/Flask, Node.js, React/Angular, TypeScript, HTML/CSS)
- Healthcare experience; familiarity with clinical datasets
- Experience with SOA and enterprise integration concepts
- Experience working in regulated industries, with knowledge of ethical AI/ML practices and compliance requirements
- Experience with Publications/patents or notable open-source contributions
- Proven excellent analysis, problem solving, and communication skills