Senior Engineering Manager, AI Platform

OpenLoop Health•Des Moines, IA

About The Position

OpenLoop is hiring a Senior Engineering Manager to build and lead its Applied AI team, which develops LLM-based agents and assistants for operations, clinical, and patient-facing teams. The role combines people leadership with technical oversight: hiring and developing engineers, owning delivery of production AI systems, and establishing standards for building, evaluating, and safely operating AI systems that use patient data. The manager is expected to provide credible input in architecture and evaluation reviews, though daily production coding is not required. The position leads production agent development from scoping through launch and ongoing operations, and partners with senior engineers on architecture for agent runtime and orchestration, retrieval, model access, and observability. It establishes rigorous evaluation practices, manages reliability and operational concerns, and ensures privacy, security, and safety requirements are met. The manager also works with Product and Operations leaders to prioritize suitable workflows and communicates capabilities, risks, and timelines to technical and non-technical stakeholders. OpenLoop describes its organizational structure as relatively flat, encouraging employees to contribute ideas and take initiative. Benefits listed for salaried positions include medical, dental, and vision plans; flexible spending and health savings accounts; flexible PTO; a 401(k) with company match; life insurance; and pet insurance.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related technical field.
10+ years of software engineering experience, including 3+ years directly managing engineers, including hiring, performance management, and career development.
Experience leading or building production LLM-based systems relied on by real users; demos, prototypes, or personal projects alone do not meet this requirement.
Experience managing senior and Staff-level engineers and earning their technical trust.
Working knowledge of agent runtime, evaluation, retrieval, and observability sufficient to review designs, challenge assumptions, and make architecture decisions.
Commitment to measuring AI system performance with evaluation data rather than relying on subjective impressions.
Strong production engineering fundamentals across testing, services, APIs, deployment, incident management, and on-call.
Experience leading teams handling sensitive data with a security-first mindset.
Comfort building in a greenfield environment and communicating clearly with technical and non-technical partners.

Nice To Haves

Experience in a regulated industry such as healthcare, finance, or insurance, including PHI or HIPAA.
Experience leading teams running production services on Google Cloud (GCP).
Experience growing an engineering team from zero or from one team to several.
Experience leading migrations between model versions or providers.
Machine learning experience beyond LLMs.
Experience leading an internal platform used by other engineering teams.

Responsibilities

Hire, onboard, and develop Applied AI Engineers across Senior, Staff, and Senior Staff levels; grow the team from one pod into multiple teams as work expands.
Drive the roadmap and execution of production LLM-based agents and assistants, from scoping through launch and ongoing operation.
Partner with Staff+ engineers on agent runtime and orchestration, retrieval, model access, and observability architecture; make technical trade-off decisions.
Require evaluation sets, regression tests, and AI-graded evaluations as part of the team's release practices.
Own production AI systems' operational health, including on-call, incident response, provider fallbacks, model-version upgrades, and AI spending by agent and use case.
Ensure AI systems protect patient data and follow least-privilege access; defend against misuse such as prompt injection and partner with Security and Compliance on HIPAA requirements.
Work with Product and Operations leaders to identify workflows suitable for agents, determine when to hand work off to people, and prioritize use cases.
Explain AI capabilities, risks, and timelines to product, operations, clinical, and executive stakeholders.
Run planning, prioritization, and delivery processes that support sustainable execution; provide direct feedback and build career paths.
Perform other duties as assigned.