[Hiring] Staff Software Engineer @hireVouch

Role Description

Lead the hands-on implementation, design and adoption of AI 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.

Qualifications

• 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.

Requirements

• 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.

Benefits

• LangGraph or LangChain named in the role descriptions, not just the skills list.
• Multi-agent orchestration in production: coordinator and sub-agents, plan-and-execute 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.