An AI transformation firm that ships working systems.
Catalyst·Wayfare is an AI transformation firm that builds production AI systems for mid-market enterprises in regulated and technical domains. We do not stop at PowerPoint AI strategy. We ship working systems in partnership our clients — and, where appropriate, their engineering teams.
The team's backgrounds span MIT, McKinsey, the White House, and some of the most respected names in tech.
Embed deep. Own the agent system.
You would embed inside the engineering organization of a leading firm in a critical-infrastructure sector, where the binding constraint on revenue is technical throughput the labor market cannot supply. You own the agent system that lifts it.
This is a forward-deployed engineering seat. GitHub history, not Salesforce dashboards. You write the code; we manage the room. The work is technically interesting (multi-agent orchestration over real domain simulators, not chatbot demos) and commercially serious (review gates tied directly to billable throughput).
We are looking for someone who codes like an IC, communicates like a PM, and navigates clients like a founder. If you have run a small thing of your own, that is a strong signal. You own architecture, the orchestration engine, and the agent design patterns we replicate at the next client.
We use what we sell.
Claude and OpenAI APIs in production. Open-source models (Llama, Mistral, more) when the data or the math points there. Cursor and Claude Code in our IDEs, daily.
Vercel, Neon, Sentry are some of our deploy surfaces. Modern infra, no six-month wait for IT to approve a tool you already use at home.
Evals are first-class artifacts, not an afterthought. Agents we trust live behind audit trails. You will not be the engineer fighting a CISO to install Cursor.
Architecture, the engine, the patterns we replicate.
- Lead architecture and build of a multi-agent orchestration platform spanning roughly seven capability agents: data ingestion, requirements retrieval, model construction, simulation orchestration, QA, report generation.
- Define interface contracts with the client's software team for integrations into the domain-specific simulation tools their business depends on.
- Design and build the orchestration engine state management, error recovery, audit trails, monitoring dashboards.
- Build on existing data foundations: a production RAG system with established retrieval infrastructure and live interaction telemetry.
- Run a tiger-team pilot loop with the client's senior engineers. Instrument, measure time savings, capture failure modes, refine.
- Embed periodically on-site with the client (heavier for the first quarter, lighter after). Travel is to a single U.S. major metro, monthly on average.
- Mentor a junior builder and shape technical hiring as we grow.
The shape of the right person.
Must-haves
- Six plus years building production software, with two plus years shipping LLM-based or agentic systems in production (not POCs that died).
- AI-pilled and full-stack. You reach for agents instinctively and have opinions about which ones. But you got fluent with code first — Cursor multiplies you; it did not teach you.
- Strong Python. Comfortable with at least one agent framework (LangGraph, Letta, custom orchestration) and the discipline to know when to roll your own.
- Hands-on experience with RAG architectures: vector databases, embedding models, document processing pipelines, retrieval evals.
- Strong intuitions for LLM evals and agent reliability. You have debugged a system that worked 70 percent of the time and gotten it to 95.
- Comfortable as the technical voice in client-facing rooms with non-engineers, executives, and domain experts, translating between them.
- Clear written communication and willingness to overlap with US Central time for core collaboration hours.
Nice-to-haves
- Prior forward-deployed or solutions-engineering experience at an AI lab, applied firm, or similar.
- Experience integrating LLMs with deterministic engineering tools, simulators, or specialized APIs.
- Cloud infrastructure (AWS, Azure, GCP) for deploying production AI applications.
- Domain exposure to regulated or technical verticals (financial services, healthcare, legal, industrial).
We are flexible on
- Years of experience if the work history shows the right shape. A strong mid-level engineer with a sharp track record of shipping agentic systems will be considered.
- Geography. See logistics above.
To the founder.
Reports to the Catalyst·Wayfare founder.
Three things, by email.
Send to talent@catalyst.wf with "Lead AI Engineer - Agentic Systems" in the subject line:
- A short cover letter. Who you are, what you have shipped, why this seat.
- A short note, three to five paragraphs, describing a multi-agent or production LLM system you built. What worked, what broke, what you would do differently.
- GitHub, code samples, or CV — optional, only if they sharpen the above.