Senior AI engineers, embedded.
When you need senior AI/ML engineering capacity now and don’t want to spend six months hiring, we embed a forward-deployed pod into your team — same sprint board, same Slack, same code-review bar. The model behind every Gigabit engagement, available on its own.
Staff aug usually means throwing work over a wall. Not here.
The offshore body shop gives you a résumé, no oversight, and code you have to rewrite. The Cheap-Vendor Tax. An embedded Gigabit pod is the opposite: named senior engineers, AI-first, with our own technical oversight, who work inside your process and ship from Sprint 1.
A small, senior team.
Sized to the work — not a roster you have to manage.
AI / agent engineers
Built RAG pipelines, multi-agent systems, and the structured-output plumbing that survives production.
ML / data engineers
Pipelines, retrieval, embeddings, and the data hygiene most AI projects skip until it breaks.
MLOps engineers
Evals, observability, versioning, and the reliability layer that keeps agents healthy.
A delivery lead
Owns scope, cadence, and the relationship — so you have one accountable point of contact.
Inside your team, not over a wall.
On your sprint board.
Same Slack, same GitHub, same code-review bar. The pod works inside your environment, not over a wall.
Async-first, with overlap.
Built around 2–3 hours of US-Eastern overlap, so handoffs are clean and momentum never stalls.
Senior, not staffed-up.
You get engineers who've shipped production AI — not juniors who learned a tool last quarter.
You see the work before you commit.
The cost advantage is real — senior engineering without Bay-Area overhead — but it's the floor, not the pitch. The pitch is named engineers who ship.
By role and seniority. 2–4 week trial, no lock-in, 2-week replacement guarantee, 30-day notice. Assembled in two weeks, first PRs by Day 5, full sprint velocity by Week 4.
We are nota staffing agency filling seats with whoever’s on the bench. A pod is a small, senior, accountable team with a delivery lead — forward-deployed to ship, and to level up your engineers while it does.
Questions about embedded pods
What does an embedded AI engineer cost?
$3,000–$7,500 per engineer per month, depending on role and seniority, with a 2–4 week trial and no lock-in.
How fast can a pod start?
Two weeks to assemble, first pull requests by Day 5, full sprint velocity by Week 4.
How is this different from offshore staff augmentation?
Named senior engineers, AI-first, with Gigabit's technical oversight, working inside your process and code-review standards — not résumés thrown over a wall. There's a trial and a replacement guarantee.
Put a forward-deployed pod on your roadmap.
Tell us what you're trying to ship. We'll assemble the right small, senior team — usually within two weeks.