Staff Software Engineer - AI Engineering

hireVouch; Canada; Remote; CAD 150,000 - 190,000; Full time

Job summary

hireVouch is hiring a Staff Software Engineer, AI Engineering. This remote Canada role leads hands-on AI agent design across billing and revenue platforms, with Python and Java services, LangGraph, and LangChain at the core.

You will drive production-grade AI services, ensure reliability, security and observability, and mentor engineers while collaborating with product and data teams.

Qualifications

• 8+ years building scalable distributed systems.
• 2–3+ years shipping production AI applications with LLMs and business workflows.
• Hands-on with LangGraph, LangChain, LlamaIndex or CrewAI; strong RAG and tool-calling knowledge.
• Distributed/event-driven systems using Kafka or similar.
• Proficient in multi-agent orchestration and memory/state management.

Responsibilities

• Lead hands-on implementation and design of AI agents.
• Set standards for reliable, secure AI systems across teams.
• Connect Python agents to Java/Spring Boot services via APIs and events.
• Build auditable AI workflows with fallbacks and human review.
• Mentor junior/mid-level engineers and improve latency, throughput and cost.

Skills

Distributed systems

AI applications design

Multi-agent orchestration

LangGraph / LangChain

RAG & tool calls

Observability & reliability

Mentoring engineers

Education

Bachelor's degree or higher

Tools

LangGraph

LangChain

LlamaIndex

CrewAI

Kafka

Pub/Sub

GCP

Vertex AI

Java

Spring Boot

Python

Docker

Kubernetes

PyTorch

HuggingFace

Job description

• Remote, Canada
• Computer/Software

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

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.