By Simon Gonzalez de Cruz (follow the build in public on X @KyaniteLabs_). 2026-08-24. The honest version, no resume inflation.
You do not need a software engineer title to do forward deployed work, and my path is the proof. I hold no FDE badge from an AI lab. What I have is twelve years of deploying enterprise systems for real organizations, then a period of building and measuring AI on my own hardware with everything published. That combination is the job. This post is the path exactly as it happened, with the artifacts, and without inventing anything.
Let me be precise about what I am not claiming, because this industry runs on inflated claims. I was never employed as a software engineer. I never worked at Palantir or an AI lab. If you want those lines on a resume, this is not your post. If you want to do the work and be findable by it, it is.
Phase one: twelve years of deployments nobody called FDE
Enterprise learning systems: Workday, SuccessFactors, Cornerstone, for organizations of more than 8,000 people. That work was forward deployment in everything but name. Configuration for a real customer's strange requirements. Integrations with whatever systems already existed. Data migrations where an error means a person's training record disappears. Rollout, training, and then ownership in production, for years.
The transferable skills from that world: reading a messy real workflow and finding where it actually breaks, writing for non-engineers, and treating go-live as the beginning, not the end. If you have done this kind of work in ERP, CRM, medical systems, or industrial software, you already have phase one. You just have not labeled it.
Phase two: own the whole stack, then measure it
The modern version of the job needs AI-specific skills. I built them in public, on my own hardware, because that removes every excuse. The rig is a $1,400 GMKtec EVO-X2 mini-PC running a 27B model locally. Everything below is linked and reproducible.
- Serving in production. The model runs always-on with watchdogs, restart recovery, and queue discipline. The complete measured serving story, with the upstream llama.cpp bug we caught, bisected, and validated the fix for, is on this blog.
- Benchmarking with intervals. 93% HumanEval (28/30, frozen subset) and 67% LiveCodeBench-30 (20/30, Wilson 95% interval 49 to 81). Published with conditions, seeds, and raw logs.
- An eval with my name on it. delegation-bench: 495 trials, 29 cells, certified floors, a sabotage cell, walk-away certification. This artifact does more work than any title. Details in evals are the FDE skill nobody lists.
- Shipped open source people actually use. A video-editing MCP server, Kinocut, with merged upstream contributions. Integration glue is the daily texture of forward deployed work.
The exact recipe, if you want to copy it
Step one: take a domain where you already know what "working" means. Step two: buy or rent hardware you control, or use cloud if you must, and run a real model workload on it. Step three: measure something honestly and publish the number with its interval and its conditions. Step four: build an eval for a decision someone actually makes. Step five: keep the system running unattended and write down what breaks.
Do those five things and you will have what interviewers for these roles are actually probing for: evidence you can find the leverage point, build with evals, and own it in production. None of the five steps requires a title. All of them produce artifacts with your name on them.
The honest gaps
What this path does not give you: the internal referral networks of a big lab, experience with frontier-scale training runs, and the specific brand names that recruiters filter on. Those are real costs and I will not pretend otherwise. The compensation is that everything you claim is checkable, and the buyers who care about checkable are the buyers worth having.
Start with the series: what a forward deployed engineer actually does, the title decoder, and the eval skill.
Want this work done in your environment instead of reading about it? Implementation intake. Every artifact cited: delegation-bench (evals), qwen38-27b-strix-halo (serving), simongonzalezdc (Kinocut and other repos). Conditions for all numbers: $1,400 GMKtec EVO-X2, Qwen3.8-27B Q4_K_XL, llama.cpp.