About the job
We're looking for a Staff AI Engineer to join us as the first dedicated AI engineer. Your core mission is to build the AI infrastructure and agentic workflows that transform how our team develops and operates software.
Responsibilities
Architect and build a centralized context layer that gives AI agents grounded, team-specific knowledge
Design and implement agentic workflows for the full development lifecycle: AI-assisted code generation, automated test creation, PR pre-review, and deployment validation
Build AI-powered operational workflows — automated incident triage, log and metric correlation, root cause analysis, and guided resolution
Develop multi-agent orchestration where parallel agents handle implementation, testing, and documentation as coordinated workflows
Set up standardized AI development environments so every engineer can work in an AI-first workflow from day one
Drive team-wide AI adoption through hands-on enablement — pairing sessions, architecture reviews, workflow demonstrations, and continuous feedback loops
Qualifications
Minimum
3+ years of significant focus on applied AI systems
Proven experience building and deploying agentic AI systems in production — agent architectures, tool integration, orchestration, and evaluation frameworks
Hands-on experience setting up AI infrastructure for end-to-end software development workflows (AI coding assistants, context engineering, automated testing)
Strong software engineering fundamentals — you build production-grade systems, not just prototypes
Deep experience with retrieval-augmented generation — document indexing, embedding strategies, retrieval pipelines, and grounding techniques
Proficiency in Python and/or JVM languages
Demonstrated ability to drive technical adoption across a team — you can demonstrate value, build trust through pairing and architecture reviews, and bring engineers along on new workflows
Preferred
Prior experience as the first or early AI engineer on a team — standing up AI capabilities where none previously existed, with the ownership and initiative of an early-stage environment
Familiarity with LLM application patterns: context engineering, tool use / function calling, structured outputs, multi-agent coordination, and evaluation / hill-climbing methodologies
Experience integrating AI into CI/CD pipelines (automated PR review, test generation, deployment validation)
Background in building operational tooling — incident response automation, log analysis, diagnostic workflows