Applied AI Systems Engineer
Core
Improving real-world behavior of AI systems by tracing runtime issues, building agent simulators, designing LLM evals, and interfacing with client data.
Role type
Applied AI Systems Engineer (Agentic AI)
Builds
AI agent platform infrastructure, QA tooling, and evaluation frameworks for healthcare automation
Domain
Healthcare technology + Agentic AI systems
Deliverable
production ML models | infrastructure
Required skills
Node, TypeScript, Python, prompt engineering, LLM evals, agent orchestration, debugging async/multi-step systems, data transformation pipelines, distributed system design, Git
Preferred skills
multi-agent systems, TTS/NLP pipelines, structured output validation, LangChain-style orchestration, testing frameworks
Technologies
Node, TypeScript, Python, Cursor, GitHub Copilot, Claude, LangChain
Responsibilities
Trace and fix runtime bugs with regression tests, design evaluation datasets for realistic workflows, build internal QA and simulation tooling, normalize and transform messy client data, set up automatic testing and latency tracking infrastructure, create observability dashboards for agent behavior
Seniority
Mid-level (2-7 years), hands-on IC
