✦ Forward Deployed Engineering · 2026

AI that
actually ships.

DashOne embeds Forward Deployed Engineers with your team for 4–12 weeks and turns your AI roadmap into production systems. Not slides. Not pilots. Production.

12+ Years shipping
production systems
4–12 Week engagements,
fixed scope
100% Hands on keys.
No slide consulting
01 · Definition

What's a Forward
Deployed Engineer?

A senior engineer who deploys with your team — not for it. Origin: Palantir. Now standard at Anthropic, OpenAI, and every serious AI infra company. The role exists because shipping AI isn't a research problem — it's an integration problem.

🛠

Hands on the code

Our engineers write production code in your repo. Review PRs. Own the deploy. The output is software that runs, not a deck that recommends.

🎯

Owns the outcome

Measured by what shipped and what moved — conversion, latency, accuracy. Not by hours billed or workshops run.

🤝

Embedded, not external

We join your Slack. Sit in your standups. Pair with your team. The handover is your team running the system — on their own.

02 · The difference

FDE vs
AI Engineer.

Both important. Different jobs. Most companies hire one and need the other.

AI Engineer Builds the model
  • Selects, fine-tunes, evaluates models
  • Lives in notebooks, evals, datasets
  • Optimizes accuracy, latency, cost per token
  • Reports to ML / Research
  • Output: a model that works on the benchmark
VS
Forward Deployed Engineer Ships the product
  • Integrates models into your product
  • Lives in your repo, your queue, your incidents
  • Designs guardrails, observability, human-in-the-loop
  • Reports to your CEO / Head of Product
  • Output: a system your users depend on

The 95% problem

MIT reports 95% of enterprise AI pilots fail to reach production. The cause isn't the model — it's the missing engineering layer between the model and the business. That layer is what DashOne's FDEs own.

03 · Engagements

What you'll ship
working with DashOne.

01

Agentic systems in production

Multi-step agents with proper orchestration, tool use, MCP integrations, observability and cost guardrails. Not demos — systems your users can rely on.

02

Context engineering

Memory, retrieval, prompt caching, and the architecture that makes responses accurate, cheap and reproducible. The work that separates shipping from prototyping.

03

Guardrails & evals

Policy engines outside the model, output validation, red-teaming, structured evals tied to your business metrics. The compliance and reliability layer.

04

Team enablement

Your team owns it after we leave. Pairing, code reviews, internal docs, runbooks. The deliverable is your engineers doing this without DashOne.

04 · Resources

Decks &
writing.

Public material from the DashOne team on enterprise AI architecture. Interactive — open on any device.

05 · About

Meet Jules.

Jules has spent 20+ years shipping compliant, delightful and production-ready software — most recently as an FDE for companies integrating AI into their core product. He works with a small number of teams at a time so engagements stay high-leverage.

Based in Mérida, Yuc. Mexico. Works globally. Available for 4–12 week engagements.

"The gap between 'generally intelligent' and 'specifically useful' is where most AI work fails. Closing that gap is the entire job."

Jules Avila Forward Deployed Engineer
06 · Start a project

Let's build
something real.

Tell DashOne about your team, your stack, and what you're trying to ship. We'll reply within 48 hours.