Hire Forward Deployed Engineers who embed in your team.
Hire senior engineers who join your standups, work in your codebase, and ship production AI on your stack. Not consultants who deliver a deck and leave.
Senior engineers who have embedded with more than twenty companies worldwide and left working systems behind.
Why most AI projects stall
Strategy decks do not ship. People in the room do.
Most AI projects never make it from pilot to production. It is rarely a knowledge gap. It is an execution gap. The roadmap is sound; nobody owns the last mile of getting it live, integrated, and reliable inside your real systems.
Generic consultancies bill you for discovery and hand over a PDF. Staffing marketplaces rent you a contractor and leave you to manage them. Neither closes the gap. A Forward Deployed Engineer does, by building it in your environment, alongside your team.
The short answer
What is a Forward Deployed Engineer?
A Forward Deployed Engineer (FDE) is a senior engineer who embeds directly in your team to design, build, and ship production AI systems. The model was popularised by Palantir and is now used by OpenAI, Anthropic, and a new generation of engineering-first firms.
The difference from a normal consultant is simple: an FDE still writes and ships production code. They join your standups, work in your repos, integrate with your data and tools, and leave you with systems your team owns and can run without us. At Sarvaswa, our FDEs are remote-first and deeply integrated into your workflow, with on-site time when an engagement calls for it.
Where Sarvaswa FDEs are different
Not a deck. Not a rented body. Not a wrapper.
We own the outcome, not the timesheet
Staffing shops supply a contractor and hand you the management problem. Our FDEs are accountable for a working system in production. The engagement is scoped to outcomes and measured against your real use cases.
Tech-agnostic.
We pick the best tool for your problem. Open or commercial, any cloud. Never a stack we are paid to resell, and never a dependency on us once the work is done.
Engineers who build for a living
No junior analysts behind a partner's pitch. You get senior engineers who train custom LLMs, build agentic systems, and run them in production. The same people who ship are the people in your standups.
What you get
What our Forward Deployed Engineers ship.
Production AI systems
Custom LLMs, agents, and automations running in production. Not a demo.
AI agent developmentBuilt to pass security review
Architecture agreed with your security team before code ships. SOC 2, HIPAA and regional requirements scoped upfront.
Enterprise AIIntegrations and MCP servers
Claude Skills and Model Context Protocol servers that connect AI to your tools, databases, and APIs.
Claude EnablementData and evaluation pipelines
Retrieval, prompt registries, and evals that keep the system reliable as it scales.
The fine-tuning explainerKnowledge transfer by default
Your team builds alongside the FDE and can run and extend the system after handover.
How hiring worksCost and performance tuning
The right tradeoffs on model, latency, and spend. On a recent regulated workload we cut compute by about 40%.
The engagement, in use casesHow to hire a Forward Deployed Engineer
From first call to embedded in two weeks.
Scope (1 call)
A focused conversation on your highest-value use case, your stack, and your timeline. No discovery invoice.
Match
We assign a senior FDE (or a small pod) with the right domain and engineering fit. You meet them before you commit.
Embed
Your FDE joins your standups, Slack, and repos. PoC against your real data in 2 to 4 weeks. Production in 8 to 14 weeks.
Hand over
Our involvement decreases by design. We leave your team self-sufficient, with the runbook and the repository, and stay available when you need us.
Compare the options
FDE vs hiring in-house vs a traditional consultancy.
In-house AI hire
Traditional consultancy
Recommended
Sarvaswa FDE
In-house AI hire
- Time to value
- 6 to 9 months (recruit and ramp)
- Deliverable
- Depends on the hire
- Seniority on the work
- One person's ceiling
- Tech choices
- Limited by one skillset
- What you have at the end
- Runs on one person
- Risk if it does not fit
- Severance, re-hire
Traditional consultancy
- Time to value
- Months of discovery first
- Deliverable
- Strategy deck
- Seniority on the work
- Juniors under a partner
- Tech choices
- Whatever they resell
- What you have at the end
- Often locked in frameworks
- Risk if it does not fit
- Sunk fees, no system
Recommended
Sarvaswa FDE
- Time to value
- PoC in 2 to 4 weeks
- Deliverable
- Production system on your stack
- Seniority on the work
- Senior engineers who ship
- Tech choices
- Best tool for the job
- What you have at the end
- Documented, run by your team
- Risk if it does not fit
- Scoped engagement, working system
Selected work
Embedded engagements, measurable results
A predictive GPU auto-scaling engine paired with a domain-trained SLM.
A domain-trained SLM met the client's compliance requirement without a commercial AI API. Predictive autoscaling forecasts load and scales the GPU fleet ahead of demand.
~40%
Lower monthly compute
100%
Inference inside client infra
24/7
Observability
Forward Deployed Engineer FAQ.
New to the role? What is a forward deployed engineer, and when should you hire one is the plain-English explainer. Still deciding how to add AI engineering capacity at all? Our guide to hiring AI developers compares an embedded team against a permanent hire, freelancers, and a traditional consultancy.