Senior Software Engineer in Test (AI Agentic Systems)

Collective Health•Lehi, UT

7h•Hybrid

About The Position

At Collective Health, we’re transforming how employers and their people engage with their health benefits by seamlessly integrating cutting-edge technology, compassionate service, and world-class user experience design. This is not a traditional QA role. You will be the quality owner for an LLM-based multi-agent pipeline that autonomously adjudicates health insurance claims for self-funded plan sponsors. You are building a Three-Tier Evaluation Framework to ensure our Gemini-powered agents reason correctly, call tools accurately, and produce DOL-ready outcomes. You will work at the intersection of Vertex AI, healthcare compliance, and high-scale data engineering. Your work directly determines whether claims are paid correctly and whether the company can withstand a Department of Labor (DOL) or state DOI audit. The stakes are real, the domain is hard, and the problems are genuinely novel.

Requirements

Python SDET Expertise: Expert in Python and pytest, specifically building custom mocking frameworks for external APIs (Vertex AI/ADK).
AI/LLM Observability: Hands-on experience with Vertex AI Experiments, Auto-SxS, and Cloud Logging for trace analysis.
Data Literacy: Expert-level SQL (BigQuery) and Pandas skills to "diff" massive datasets and identify adjudication discrepancies.
Prompt Engineering for QA: Ability to analyze "System Instructions" and refine prompts based on failed test cases to close logic gaps.
Architectural Testing: Experience testing multi-layer systems involving RAG (Vertex AI Search), state management (LangGraph), and function calling.

Nice To Haves

Healthcare/Claims Domain: Familiarity with claims adjudication concepts (pend reason codes, COB, eligibility, stop-loss).
Compliance Knowledge: Understanding of HIPAA/PHI handling and writing test evidence for regulatory bodies (DOL/DOI).
Human-in-the-Loop Testing: Experience in "Shadow Mode" monitoring—comparing agent decisions against human expert (MCA) baselines.

Responsibilities

Outcome Evaluation (The "What"): Golden Set Governance: Build and maintain a versioned library of "Grounding Data" results by working with senior claims examiners to define "Ground Truth." Model-as-a-Judge Automation: Design automated "LLM-grading-LLM" workflows using custom rubrics to score factual grounding and policy compliance. Semantic Assertion Framework: Develop testing libraries that move beyond string matching to validate semantic equivalence and numerical accuracy in agent outputs.
Trajectory Evaluation (The "How"): Function-Call Auditing: Use Vertex AI traces to programmatically verify that mandatory tools (via MCP) were invoked with correct arguments. Orchestration Logic Validation: Assert that agents respect defined priorities across the four architectural layers: Data & Knowledge, Orchestration, Agentic Reasoning, and Tooling. Reasoning Trace Auditing: Ensure every autonomous decision is traceable to a specific SOP sentence and a live API data point.
Continuous Automated Regression (The "Always"): CI/CD Integration: Every prompt or model update in Vertex AI Prompt Management must trigger an automated regression run. Auto-SxS: Own the automated pairwise comparison process to detect logic drift between "New" and "Production" agent versions. Mocking & Resilience: Build a Vertex AI/ADK mocking layer to simulate model responses, allowing for thousands of logic tests in seconds with zero API costs.