Head of AI Engineering

About levelheaded

Levelheaded is reimagining how disputes get solved at scale. We blend humans and AI to power a resolution process that’s fast, fair, and human. Our AI engine, lev, is not a feature—it’s the connective tissue of our platform: conversations, suggesting possible paths to resolution, and helping people move on with life.

We're not building legal tech. We’re building resolution infrastructure and a brand new category of solving problems at scale with a native AI / LLM build.

Why This Role Matters

We're looking for a hands-on, high-context Head of AI Engineering to own the architecture, framework and delivery of our multi-agent AI system. This is a high-performing player-coach role for someone who thrives in ambiguity, builds quickly, and loves turning human problems into structured, scalable AI native systems.

For the right candidate, this role has a clear track to CTO.

What You’ll Do

  • Own the AI system architecture and build. Including multi-agent workflows for our intake, tone scoring, sync and async resolution paths, and outcome generation/training

  • Build and deploy custom agents using Claude, RAG pipelines, and CrewAI, among others, specific to our current ICPs

  • Write Python and orchestration code for LLMs, while supporting backend integrations (Node.js, Postgres, AWS)

  • Lead prompt versioning, real-time evaluation, and HITL escalation logic

  • Partner deeply with product to translate real problems into useful, empathetic AI workflows that scale fast, have training and feedback loops and create unique competitive advantage in today’s fast-moving legal tech space

  • Extend and optimize our RAG pipelines to be emotion-aware and issue-specific

  • Build internal tooling to monitor prompt health, system trust, and conversation-level insights

  • Collaborate with devs, PM, UX, and founders to ship product with speed and precision

  • Define and grow the engineering culture that will carry this platform to scale

You Might Be a Fit If…

  • You have 8–12+ years engineering experience, with 3+ years leading AI/LLM-heavy builds

  • You’ve built and shipped AI-first products (ideally using CrewAI, LangGraph, or RAG stacks)

  • You’re fluent in Python, Claude/OpenAI APIs, vector DBs, prompt engineering, and eval tooling

  • You’ve worked full-stack or know how to partner with frontend/backend teams

  • You’re opinionated about HITL, agentic orchestration, and vertical model fine-tuning

  • You care deeply about outcomes, not just building models

  • You’re excited to help architect our moat using proprietary tone, resolution, and feedback data in a category we are defining as the business scales

  • You can mentor other devs while staying hands-on, curious, and fast-moving

Success Looks Like: First 3–6 Months

  • Clear plan & strategy for our architecture to create a moat, feedback loops, fine-tuning, and proprietary data capture

  • Three core ‘lev’ agents shipped (Intake, Session/Mediation, Resolution) live and assisting in production cases at scale, fine-tune / train our ‘universal’ lev intake agent, our initial handshake agent

  • 75%+ of low-complexity disputes handled ‘AI-first’, with HITL in place where most impactful

  • Live async resolution preview flow integrated into our product

  • Tone monitor, trust meter, and prompt health tooling deployed

  • RAG pipelines optimized for the top 5 property dispute types

What We’re Building (and Why It Matters)

From Mo, our CEO:

"We’re not building a legaltech platform or feature … we’re creating a brand new category and a resolution engine that solves problems at scale.
We believe empathy and AI can coexist. Lev isn’t just AI automation; it’s how we scale access to justice.”

Current Stack

  • AI/LLM: Anthropic Claude, OpenAI, Tool Usage, Agents, Prompt engineering

  • Backend: Node.js, Postgres, AWS (Lambda, S3, SES, Bedrock)

  • Frontend: Next.js, React, TailwindCSS

  • Infra: GitHub Actions for CI/CD

Bonus If You’ve…

  • Built agentic workflows across legal or similar verticals

  • Led and or built a team of AI engineers in early-stage startup environments

  • Experimented with RLHF or agent-based simulation

  • Been part of a platform company that scaled fast without losing soul and created a defensible, deep data-driven moat to secure market leadership and future scale