Forward Deployed Engineer

We’re hiring Forward Deployed Engineers to take AI from a promising prototype to something people use every day. You’ll scope the problem with the client, write the code, connect it to their systems, and stay with it until it works in production.

• 4+ years writing production software, with Python as a primary language
• Shipped at least one LLM-powered system to real users (agents, RAG, tool use, or structured extraction)
• Comfortable with cloud deployment, APIs, and the unglamorous parts like auth, logging, and CI/CD
• Working knowledge of how to evaluate LLM output, beyond eyeballing it

Client-Facing

• Experience in a customer-facing technical role: consulting, solutions engineering, implementation, or a startup where you talked to users directly
• You can run a meeting, write a clear status update, and push back on a bad requirement

Nice to Have

• Experience with MCP, agent frameworks, or orchestration tooling
• Background in a regulated industry such as healthcare, finance, energy, or government
• You use AI tools daily in your own work and have opinions about which ones are worth it

These are Full time contract engagements with enterprise teams and AI-focused companies in industries like energy, healthcare, financial services, and industrial operations. Some weeks look like Back End engineering. Others look like consulting: running a working session with operators, figuring out what the data can actually support, and telling a VP what’s realistic.

If you’ve built an agent or RAG system that impressed people in a demo and then watched it stall before launch, this is the job where you get to finish it.

What You’ll Do

• Sit in discovery with client stakeholders and turn loose business goals into scoped, buildable work
• Build LLM applications, agents, and retrieval systems in Python that hold up under real latency, cost, and security limits
• Connect models to the systems the client already runs: APIs, data warehouses, internal tools, and MCP Servers where they fit
• Write evals before calling anything done, and use them to decide what to fix next
• Deploy on AWS, Azure, or GCP and own what happens after launch
• Some of the work is integration and cleanup. Better to say that now than surprise you later
• Explain trade-offs to technical and non-technical audiences, in writing and in the room
• Bring what you learn in the field back to the team so the next engagement starts further ahead