Sr Software Engineer - AI Platform

Blue Cross and Blue Shield of Nebraska•Omaha, ND

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. We have a live AI platform designed to transform how work gets done across Blue Cross and Blue Shield of Nebraska. AI Agents are helping teams move faster, reduce manual effort, surface insights, and reimagine business processes through AI-driven experiences. The platform is working. Now we need to scale it across the enterprise in a way that is measurable, governed, secure, and capable of delivering real business outcomes. This role is the connective tissue between the agents technical capabilities and the business value it creates. You'll work across business and technology teams to identify the right opportunities, shape AI-enabled solutions, and ensure every use case delivers measurable impact. You help bridge strategy, operations, and technology, turning AI experimentation into sustainable business transformation. You'll work shoulder-to-shoulder with business leaders, subject matter experts, engineers, and solution teams. They bring the business challenges and technical capabilities, while you help connect the dots, define the future state, and drive adoption at scale. Together, you will take the agent from a promising platform to a core capability that reshapes how BCBSNE operates. 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 based on business needs.

Requirements

Bachelor’s degree and 4-7 years of experience in software engineering. An equivalent combination of education and experience may be substituted to meet this requirement.
Experience operating LLM-based systems in production—not building demos, not writing blog posts about evaluation. You’ve been the person who gets paged when the AI stops working correctly, and you’ve built the systems that prevent that page from happening.
2–3 years in ML/LLM operations, MLOps, or AI platform engineering with production systems serving real users
Built evaluation frameworks from scratch: you’ve defined what “correct” means for an LLM system, created the golden datasets and metrics to measure it, and wired it into CI/CD so regressions block deployment
Deep observability experience: distributed tracing, custom metrics, alerting on AI-specific signals (drift, latency spikes, retrieval quality drops). You’ve diagnosed a subtle AI failure using your own instrumentation
Production ML pipeline experience: model versioning, prompt management, A/B testing, canary rollouts, automated rollback. You understand that deploying an AI change is not the same as deploying code
Cost optimization at scale: you’ve tracked token usage, implemented caching strategies, or built model routing that saved meaningful infrastructure spend
Experience in regulated industries (healthcare, insurance, fintech, legal)—environments where audit trails, compliance logging, and explainability are requirements, not nice-to-haves
Strong Python skills. Comfortable working in a shared codebase alongside agent engineers, contributing to shared tooling and infrastructure
Familiarity with Azure ecosystem (Azure OpenAI, Azure Monitor, App Insights, Container Apps) or equivalent cloud-native AI infrastructure
Strong knowledge of Python. Work in an existing codebase and extend it with the AI layer
Azure OpenAI, Azure AI Search, Azure Container Apps are the target stack

Nice To Haves

Experience working in an Agile/Scrum environment.
Desire to think in a creative, lateral way both conceptually and in practical application.
You must be obsessed with addressing customer needs.
Strong communication and interpersonal skills.
Experience with Azure AD AuthN/AuthZ strategies.
Experience creating Single Sign On (SSO) experiences.
Possess emotional intelligence.

Responsibilities

Platform reliability, evaluation, and AI operations.
Business opportunity identification and prioritization. Partner with leaders across Medical Management, Claims, Customer Service, Provider Services, Finance, and Corporate Operations to identify the work best suited for AI agents. Evaluate opportunities based on strategic alignment, operational impact, complexity, and expected business value. Build and maintain a roadmap of agent initiatives that deliver measurable outcomes.
Future-state workflow design. Work with business teams to rethink how work gets done in an AI-enabled environment. Facilitate process discovery sessions, identify manual steps that can be automated or augmented by agents, and help teams move beyond current-state thinking. Define future-state workflows that improve efficiency, quality, and employee experience.
Agent solution shaping and implementation. Serve as the bridge between business stakeholders and technical teams. Translate business challenges into agent-enabled solutions, help define requirements, establish success criteria, and ensure agent capabilities are aligned to real operational needs. Guide initiatives from concept through deployment and adoption.
Change management and adoption. Drive organizational readiness for AI-powered ways of working. Build stakeholder support, identify champions within business areas, facilitate training and communication plans, and help teams integrate agents into day-to-day operations. Ensure solutions are embraced and sustained long after initial deployment.
Business value measurement and optimization. Establish key performance indicators and outcome measures for each agent initiative. Track productivity gains, turnaround-time improvements, quality enhancements, cost savings, and experience improvements. Continuously evaluate results and identify opportunities to expand or improve deployed solutions.
Enterprise AI governance and scaling. Help establish repeatable frameworks for identifying, evaluating, prioritizing, and delivering agent opportunities across the enterprise. Ensure solutions align with business priorities, risk management standards, compliance requirements, and enterprise architecture principles. Create the operating model that allows agents to scale from individual use cases to enterprise-wide capabilities.
Strategic partnership and leadership engagement. Build strong relationships with business and technology leaders to drive AI strategy execution. Facilitate discussions around organizational priorities, emerging opportunities, and investment decisions. Act as a trusted advisor who helps leaders understand not just what agents can do, but how they can fundamentally transform the way work gets done.