Forward Deployed AI Engineers for Production AI and Data Projects
A forward deployed engineer (FDE) from BigData Boutique works inside your team, on your data, in your cloud, until the AI or data system you need is running in production. Not a slide deck, not a staff augmentation body: a senior engineer who has shipped the same shape of system before, backed by our search, data, and AI specialists.
We place forward deployed AI engineers for RAG and agent projects, search modernization, streaming and lakehouse platforms, and the "we have a POC and nobody can get it to production" problem. Read what a forward deployed engineer is if the term is new to you.
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When a Forward Deployed Engineer Is the Right Call
Forward deployed engineering earns its keep when the platform is powerful but generic, your problem is specific, and a slow or failed rollout is expensive. These are the situations where companies bring in our FDEs.
The AI POC that will not ship
A RAG chatbot or agent that impressed in a demo and has been "two weeks from production" for six months. An FDE adds the evaluation, guardrails, retrieval quality, observability, and cost controls that turn a prototype into a system people rely on.
A platform you bought but cannot use
Bedrock, OpenSearch, Databricks, ClickHouse, or a vector database that is paid for and under-used because nobody on the team has built on it before. Our FDEs have, many times, and work alongside your engineers until they can too.
A deadline the team cannot hit alone
A product launch, a customer commitment, or a board-level AI initiative with a date on it. An FDE embeds for a quarter, ships the critical path, and leaves the system documented and owned.
Data that is not ready for AI
The model is fine; the data is scattered, stale, or impossible to join. Our forward deployed AI engineers come from a data engineering company, so they build the pipelines and retrieval layer first and the AI on top of it.
What You Get From a BigData Boutique FDE
A senior engineer, not a bench
Every FDE we place has shipped production search, data, or AI systems for other customers. You interview them before anything is signed, and the same person stays for the engagement.
The whole company behind them
When the problem turns out to be an Elasticsearch shard strategy, a Kafka consumer, or a ClickHouse schema, the FDE pulls in the specialist. You get one engineer's attention and a team's expertise.
Your repos, your cloud, your process
The FDE works in your repositories, your CI, your stand-ups, and your AWS, GCP, or Azure account. Nothing is built in a sandbox you have to migrate out of later.
An exit plan from day one
Documentation, runbooks, and pairing with your engineers are part of the scope. The goal is a system your team owns, not a dependency on ours.
How a Forward Deployed Engagement Works
Scoping call
A 30-minute call about the outcome you need, the stack, and the timeline. We tell you honestly whether an FDE is the right model or a fixed-scope project would serve you better.
Meet the engineer
You interview the proposed FDE and agree on allocation (part-time or full-time), duration, and a written plan with milestones.
Embed and ship
The FDE joins your team the following week, with weekly progress against the plan and specialists pulled in as needed.
Hand over or extend
At the end of the term you keep the system and the documentation. Extend by the quarter, or move to an hours bank or production support plan.
The FDE is our embedded engineer model. See how our engagement models and pricing work.
Projects Our Engineers Have Shipped
Work delivered by embedding with the customer's team. More on our customer stories page.
Max Security partnered with BigData Boutique to build SCOUT AI, an AI-powered agent on OpenSearch, Amazon Bedrock, and Anthropic Claude that made proprietary intelligence instantly accessible, cutting briefing time by 79% and saving analysts 7 hours per week.
Digipal partnered with BigData Boutique to design and build an end-to-end IoT asset tracking and analytics platform on AWS from day one, going from vision to a revenue-enabling product at speed.
Forward Deployed Engineer FAQ
What is a forward deployed engineer?
A forward deployed engineer (FDE) is a senior software engineer who works inside a customer's environment to design, build, and operate a solution, rather than shipping generic features from a vendor's product team. The role started at Palantir and is now common at AI labs and data platform companies. BigData Boutique's FDEs embed with your team to get AI and data systems into production.
How is an FDE different from staff augmentation or a contractor?
Staff augmentation gives you a person to fill a seat. A forward deployed engineer is accountable for an outcome, brings a playbook from having shipped the same kind of system before, and is backed by our search, data, and AI specialists when the problem crosses into their territory. The engagement has a written plan and milestones, not just a timesheet.
What kinds of projects do your forward deployed AI engineers take on?
RAG and knowledge assistants, AI agents and agentic workflows, hybrid and semantic search on Elasticsearch or OpenSearch, streaming and lakehouse data platforms, analytics on ClickHouse, and taking existing AI proofs of concept to production. All of it runs on your data in your cloud, typically AWS Bedrock, GCP, or Azure.
How long is a typical forward deployed engagement?
Most placements run one to two quarters, part-time or full-time, with the option to extend. Shorter engagements usually fit better as a fixed-scope project; we will tell you which on the scoping call.
How is a forward deployed engineer priced?
Monthly, by allocation (for example half-time or full-time), with the rate set by seniority and duration. We do not publish list prices; you receive a written quote, and interview the engineer, before anything is billed. See our engagement models page for how this compares with hours banks and fixed-scope projects.
Will the FDE work in our tools and our cloud account?
Yes. The engineer works in your repositories, CI, ticketing, and chat, and in your AWS, GCP, or Azure account under your security policies. Nothing is built outside your environment.
What happens when the engagement ends?
You keep the system, the code, the documentation, and the runbooks. Knowledge transfer and pairing with your engineers are part of the scope from the first week, so the handover is a milestone rather than a surprise.
Related: What is a forward deployed engineer? · AI consulting · AI agent development · RAG development services · AI Launchpad · Data engineering consulting · Engagement models
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