Senior Software Engineer - Applied AI

Blue Cross and Blue Shield of Nebraska•Omaha, MO

1d•Hybrid

About The Position

At Blue Cross and Blue Shield of Nebraska, we are a mission-driven organization dedicated to championing the health and well-being of our members and the communities we serve. Our team is the power behind that promise. And, as the industry rapidly evolves and we seek ways to optimize business processes and customer experiences, there’s no greater time for forward-thinking professionals like you to join us in delivering on it! As a member of Team Blue, you’ll find purpose, opportunities and the support you need to build a meaningful career and make a powerful impact in our community. Health insurance is one of the most confusing, high-stakes experiences people navigate. Members don’t understand their coverage, can’t predict costs, and struggle to get answers when it matters most. We’re building the AI platform that changes that—a member experience that feels like having an expert in your corner. It’s already live. It’s already helping people. And it’s just getting started. The next chapter is multi-agent: specialized AI agents collaborating to answer clinical questions, navigate prior authorizations, close care gaps, and guide members through their benefits. The orchestration architecture, the agent integration patterns, the evaluation systems that keep it all trustworthy at scale—that’s what we’re building together. You’ll shape how it’s designed and set the standard for how it grows. The ideal candidate will live within driving distance of the Omaha, Nebraska office. This position allows remote flexibility but will have 1 day per week in the office. If living in one of our approved states (Florida, Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and Texas) – this person may travel to our headquarters.

Requirements

Bachelor’s degree and 4-7 years of experience in software engineering.
Experience with agentic AI in production—not LLM wrappers, not single-agent chatbot demos, not RAG tutorials. Systems with multiple agents, explicit state management, and real users depending on the answers being correct.
2–3 years building and operating production LLM or agentic AI systems.
Ability to describe specific decisions made in a multi-agent system: state management approach, how agent failures mid-conversation were handled, how the system was kept governable as agents were added.
Experience with LangGraph, AutoGen, or custom graph-based orchestration used in production.
Experience debugging retrieval quality problems, iterating on chunking strategies, and dealing with stale knowledge bases in a live system with real users.
Experience building evaluation systems from scratch: golden datasets, automated regression, domain-appropriate accuracy metrics.
Experience in healthcare, insurance, fintech, or legal domains—AI systems where a wrong answer has consequences, and safeguards were designed that make accuracy non-negotiable.
Strong knowledge of Python.
Experience with Azure OpenAI, Azure AI Search, Azure Container Apps.

Nice To Haves

Experience working in an Agile/Scrum environment.
Desire to think in a creative, lateral way both conceptually and in practical application.
Obsessed with addressing customer needs.
Strong communication and interpersonal skills.
Possess emotional intelligence.

Responsibilities

Agent design and orchestration.
Design and build the LangGraph-based system that classifies member intent and routes to the right agent.
Design the standardized pattern that makes any agent pluggable: input schema, output schema, confidence signaling, latency SLA, fallback behavior.
Manage context across turns for multi-turn conversations that maintain full member context across agent handoffs.
Implement a guardrail and safety layer including clinical safety boundaries, PHI handling at the LLM layer, and response normalization.
Define the RAG pipeline architecture including ingestion strategy, chunking approach, hybrid search configuration, and retrieval quality standards.
Develop the prompt architecture including versioned prompt registry, structured output contracts, and few-shot pattern design.