Bring senior AI engineers into your team without the long hiring process

An AI engineer is an individual who joins your team and works for your engineering lead, so you decide what they do and you own the outcome. A Forward Deployed Engineer is a senior engineer embedded in your environment who’s accountable for the system working in production. A managed pod is a small team, usually an architect, engineers, a product manager and QA, that delivers a defined scope as one unit. We place all three, as an Embedded AI Engineer, an FDE Sprint and an AI Engineering Pod, so the choice depends on whether you’re missing hands, experience or a whole team.

A contractor or an augmented engineer fills a gap in capacity, and you manage them. A Forward Deployed Engineer is meant to own the result: a working system in production, built with your data. Some firms use the title for ordinary hourly staffing, so it’s worth asking what the engineer is accountable for. Our FDE Sprint (8 weeks) is scoped around a deployed system and a handover to your team.

It depends on how long you need the capability and how fast you need it. Hiring costs a salary plus the months it takes to find someone, and the cost of a wrong hire. An outside team costs more per month and starts sooner, and it ends when you decide it does. Our free 90-minute session ends with a view of whether to hire, embed or train for your roles, and what each would cost over two years.

Most of it is priced monthly, because you’re paying for people’s time. An Embedded AI Engineer, for AI or data engineering work, is $20K to $50K a month for 3 to 12 months. A forward deployed engineer in our FDE Sprint is $40K to $100K over 8 weeks. A managed AI Engineering Pod starts at $35K to $60K a month on a 3-month minimum, or $55K to $90K a month for a Full Stack pod that covers a product from design to deployment. Our AI Team Starter Pack, a whole managed team for 3 months, is $60K to $120K.

The vendor should replace them without you carrying the cost, and the terms should be written down at the start. If a named engineer of ours moves on, we provide a named replacement and a paired handover at our cost, and the notice period is written into the contract.

Yes, and that’s a common reason to bring one in. The usual approach is to start with embedded engineers, learn what roles and pay you need, and then hire. Our AI Practice Build-Out (3 to 4 weeks, $25K to $45K) defines the roles, hiring profiles, interview loops, a 90-day onboarding plan and a reference tooling stack, so you can hire with a plan and run the team yourself. If you need a product shipped while you hire, the AI Team Starter Pack gives you working capability in about three months.

If you can hire the engineers you need in the time you have, and you already have a senior person who can direct the work, you probably don’t. An outside engineer works best when someone inside can scope and review the work, so a team with no AI lead is usually better served by a pod or a forward deployed engineer than by one engineer. If the real gap is knowing what to build, advice comes first, and that’s where our AI Strategy, Governance & Compliance work starts.