Staff Software Engineer - AI Engineering

What they're building

A newAI-native billing platform built from scratch on GCP. It replaces the manual work of

US medical billing (claims, eligibility checks, prior authorization, remittances) with LLMs,

orchestrated agent workflows and RAG. The hiring manager is a Senior Director of

Engineering for Billing & Platform; he runs first interviews himself. The full loop includes

three technical challenges; walk candidates through that up front, because our previous

top candidate withdrew once he saw them.

Stack: Python, LangGraph, LangChain · GCP, Vertex AI · Kafka, Pub/Sub · Java, Spring Boot

· pgvector, Pinecone, ChromaDB · Docker, Kubernetes, FastAPI.

The role

Lead the hands-on implementation, design and adoption ofAI agents across the billing

and revenue-cycle platform. A senior IC who influences technical strategy across teams

and sets standards for reliable, secure AI systems.

Own hands-on implementation of production AI agents and agentic workflows, mainly

in Python (LangChain, LangGraph).

Design, test and operate AI services that combine agent orchestration, APIs, retrieval

and models for real customer use.

Connect Python agents to Java/Spring Boot platform services through APIs, events

and contracts; contribute in both.

Integrate LLMs, ML models and enterprise data with strong patterns for reliability,

observability and security.

Build bounded, auditable AI workflows with fallbacks, evaluation, authorization

boundaries and human review.

Turn workflow pain points into software with measurable impact, alongside product,

design, data and ops.

Review code across AI services and Java codebases; mentor junior and mid-level

engineers, including Java-first ones.

Improve latency, throughput, quality and cost across agent workflows and backend

services.

Language bar: expert in Python or Java, working proficiency in the other. Python-first

with real Java comfort is ideal. Java-first with deep Python AI work and hands-on

LangGraph also works.

Requirements

8+ years building scalable distributed systems.

2-3+ years designing, shipping and maintaining production AI applications that

combine LLMs, application logic and business workflows.

Hands-on with LangGraph, LangChain, LlamaIndex or CrewAI; deep understanding of

RAG, tool calling, state management and human-in-the-loop.

Distributed and event-driven systems with Kafka or similar.

Multi-step agents with tools, retrieval, memory, structured outputs and explicit

control flow, and the ability to explain the trade-offs.

Production AI safeguards: observability, latency and cost monitoring, automated

evaluation, fallbacks.

Cloud-native on AWS, Azure or GCP.

Preferred: healthcare IT, RCM, billing, claims, fintech or another regulated domain (the

HM estimates a 6-18 month ramp without it) · autonomous multi-agent systems with

multi-tier memory · knowledge graphs or enterprise vector search · PyTorch, HuggingFace,

embeddings or fine-tuning · founding engineer or principal architect who drove org-wide

AI adoption.

What wins here

LangGraph or LangChain named in the role descriptions, not just the skills list.

Multi-agent orchestration in production: coordinator and sub-agents, plan-andexecute patterns.

Stateful long-running workflows with retries, failure recovery and partial resumption.

RAG ownership with detail: chunking strategy, embeddings, hybrid retrieval,

evaluation.

LLM evaluation method: golden datasets, LLM-as-judge, RAGAS, Langfuse, Arize. This

was the most common gap in our screens.

Kafka or Pub/Sub in production, GCP or Vertex AI, MCP in production, AI coding tools

in daily use.