Applied AI Engineer

OpenLoop Health•Des Moines, IA

About The Position

OpenLoop is building the future of healthcare infrastructure. We are hiring for this role at the Senior, Staff and Senior Staff levels. Apply once; we will calibrate level during the process based on the scope you have carried and the depth you bring, not on years alone.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related technical field
Proven Engineering Experience: 5 to 15+ years of software engineering experience.
Production LLM Experience: Hands-on experience building and running LLM-based systems in production that real users relied on, not only demos, prototypes or personal projects. Senior Staff candidates typically bring 2+ years.
Core AI Depth: Real hands-on depth in at least one core area: agent runtime, evaluation, retrieval, or observability and cost. At Staff and Senior Staff we look for depth in agent runtime or evaluation, and at Senior Staff a second deep area plus working knowledge across the rest.
Evaluation Rigor: Experience measuring whether an AI system works with evaluation data, rather than "it seemed to work."
Engineering Fundamentals: Strong production engineering across testing, debugging, services, APIs, deployment and on-call.
Data & Security Fluency: Experience handling sensitive data with a security-first mindset.
Comfort working in a greenfield space, and clear communication with technical and non-technical partners

Nice To Haves

Regulated-Industry Experience: Healthcare (PHI, HIPAA), finance or insurance.
GCP Experience: Running production services on Google Cloud.
Model Operations: Experience migrating a system between model versions or providers.
Classic ML Background: Machine learning experience beyond LLMs.
Internal Platforms: Built a platform used by other engineering teams.
Team Leadership: For Senior Staff, experience leading a small team's technical direction from zero and growing a single pod into multiple teams.

Responsibilities

Ship Production Agents: Build and run LLM-based agents and assistants that real users depend on, not prototypes.
Agent Runtime & Orchestration: Build how agents run, call tools, hold context and hand off work, including failure handling, retries, timeouts and sandboxing.
Evaluation: Build evaluation sets, test harnesses, regression tests and AI-graded evals so the team can show "this got better" with numbers and catch regressions before users do.
Model Access & Operations: Work across multiple model providers, manage version upgrades and plan fallbacks for when a provider fails.
Observability & Cost: Trace what agents did and why, monitor behavior in production, and attribute AI spend to each agent and use case.
Grounding & Retrieval: Connect agents to real, messy company data through retrieval (RAG), context design and prompt engineering, so answers are based on facts.
Workflow Design: Work with operations teams to decide which steps of a human workflow an agent can do reliably, and when it should escalate to a person.
Security, Privacy & Safety: Keep patient data (PHI) protected in every AI system, control what agents can access, and defend against misuse such as prompt injection.
Cloud & Data Foundations: Build on GCP and partner with the Data Platform teams who provide the governed data agents use.
Raise the Bar: Contribute to code and design review, and help engineers who are newer to AI.
Cross-functional Work: Explain AI trade-offs in plain language to product, operations and clinical stakeholders, including when the honest answer is "the AI isn't ready for this yet."
Other duties as assigned.