This is Engineering at Lattice
At Lattice, we build software that helps people and organizations thrive. Our AI Engineering team defines how intelligence works across our platform - how AI systems are measured, improved, and trusted in production.
This Staff-level role shapes the foundations that determine AI quality, reliability, and impact at scale.
What You Will Do
You will architect and scale the infrastructure that powers AI quality, reliability, and reuse across Lattice.
AI Evaluation & Quality
- Design and scale an end-to-end AI evaluation framework spanning offline evals, production tracing, and human feedback loops.
- Define meaningful performance metrics (task completion, hallucination, response quality, engagement, business impact) and build the datasets and automated scoring systems that prevent regressions.
- Identify and quantify the drivers of agent quality improvement and set methodological standards for evaluation across the organization.
Agent Architecture & Production Systems
- Architect reusable agent infrastructure (multi-turn workflows, LLM DAGs, recommendation systems, standardized topologies) using LangGraph or comparable frameworks.
- Build and scale RAG pipelines, vector retrieval systems, and production-grade AI infrastructure with strong reliability, observability, and performance.
- Make principled build-vs-buy decisions across LLM providers, agent frameworks, and evaluation tooling, balancing capability, cost, latency, and risk.
- Engineer AI systems as reusable internal platforms that multiply product engineering velocity at Lattice.
Technical Leadership
- Own projects end-to-end: scope, design, execution, and delivery.
- Set technical direction for agent quality and evaluation strategy across Lattice engineering teams.
- Lead rigorous discussions on AI system design and evaluation methodology.
- Raise the AI engineering bar through mentorship, code review, and clear technical communication across engineering and leadership.
What You Will Bring to the Table
Experience
- 8+ years of professional experience writing and maintaining production-level code, with 5+ years in designing, delivering, and operating AI/ML systems in production.
- Deep production experience with LLM systems (prompting, RAG, agent orchestration, evaluation frameworks, fine-tun