AI Agent Engineer — Compliance

About Hipp

Hipp is an AI-native healthcare platform built to modernize operations for non-hospital healthcare providers. Our platform brings clinical workflows, billing infrastructure, scheduling, documentation, patient engagement, and operational data into a single intelligent system.

Healthcare organizations operate within detailed payer, regulatory, clinical, and documentation requirements. Hipp helps practices manage that complexity through software designed around real provider workflows, with AI and automation embedded throughout the platform.

Role Overview

We’re looking for an AI Agent Engineer to build systems that help healthcare practices operate accurately, consistently, and in alignment with regulatory and payer requirements.

You will develop agents that can review documentation, identify missing or inconsistent information, apply configurable policies, explain findings, and guide users toward resolution. You will also build the evaluation, audit, and human-review infrastructure required for agents operating in high-stakes environments.

This role requires strong engineering judgment. Compliance agents must be helpful without overreaching, explain their reasoning without presenting unsupported conclusions, preserve auditability, and escalate appropriately when policy interpretation or human judgment is required.

What You’ll Do

• Design and ship AI agents for documentation review, workflow compliance, policy checks, and operational quality assurance
• Convert complex requirements into testable agent workflows, tools, policies, and decision boundaries
• Build retrieval systems that ground agent behavior in approved policies, contracts, payer rules, and internal guidance
• Develop structured review workflows that surface missing information, inconsistencies, exceptions, and recommended next actions
• Create clear citations, evidence trails, and audit logs for agent outputs and actions
• Design human-review and approval mechanisms for high-risk or ambiguous decisions
• Build rigorous evaluations for factual grounding, policy adherence, consistency, recall, precision, and safe escalation
• Partner with compliance, clinical, billing, product, and engineering stakeholders to define requirements
• Monitor production agent behavior and investigate failures, regressions, or unexpected patterns
• Build versioning and change-management mechanisms for policies, prompts, models, and evaluation datasets
• Apply privacy-by-design and least-privilege principles to agent tools and data access
• Contribute reusable agent infrastructure and engineering standards across Hipp

What We’re Looking For

• Demonstrated experience building AI agents, agentic systems, or substantial LLM-powered products
• Strong software engineering skills and the ability to build secure, maintainable production systems
• Experience with tool-using agents, retrieval, structured outputs, workflow orchestration, or model evaluations
• Experience grounding AI systems in authoritative source material
• A strong understanding of agent reliability, traceability, observability, and failure handling
• Ability to distinguish between probabilistic model behavior and decisions requiring deterministic enforcement
• Experience building integrations with APIs, databases, document systems, or internal services
• Proficiency in Python, TypeScript, or a comparable programming language
• Strong analytical judgment and attention to detail
• Clear written communication, particularly when explaining technical behavior and system limitations
• Comfort collaborating with domain experts to translate nuanced rules into practical product behavior

We do not require a prescribed number of years of experience. We are looking for candidates who can demonstrate meaningful work building agents or AI systems that must be accurate, explainable, and reliable.

Nice to Have

• Experience in healthcare, healthtech, insurance, fintech, legal technology, or another regulated domain
• Familiarity with HIPAA, SOC 2, healthcare documentation, payer requirements, or audit processes
• Experience building policy engines, rules systems, or case-management workflows
• Experience with document intelligence, semantic retrieval, knowledge graphs, or provenance systems
• Familiarity with model risk management and responsible-AI practices
• Experience designing red-team tests or adversarial evaluations for agents
• Experience implementing role-based access control, data retention, or audit…