Forward Deployed AI Engineer

About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of
- Offering life-changing career opportunities to talented software professionals across the Americas.
- Building highly-skilled software development teams for hundreds of the world’s greatest companies.
- Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users.
- Our 4.1-star rating on GlassDoor.
- Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and stay long enough to make it survive contact with production. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are diagnosing why retrieval quality collapsed after a data refresh. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is data, retrieval, and model behavior: you make agents trustworthy by getting the data, context, and evaluation right. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work
- Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec.
- Decomposes the problem. Outcome process decisions data systems actors constraints — before choosing technology.
- Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why.
- Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery
- Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes.
- Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually.
- Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two.
- Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success.
- Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better.
- Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You’ll Do
- Discover. Run workshops and process discovery with business, data, and engineering stakeholders. Assess data readiness honestly. Separate genuine AI problems from workflow problems wearing an AI costume.
- Structure the process and the spec. For any business process being automated, map the current state, decision points, data inputs, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably.
- Architect & design. Design agentic systems end to end with emphasis on context and retrieval architecture — data sourcing, chunking, embeddings, hybrid search, reranking, permissions‑aware retrieval, knowledge graphs where warranted — plus orchestration, MCP/tool integration, guardrails, and human‑in‑the‑loop checkpoints.
- Build. Ship working systems — agents, retrieval services, evaluation tooling, and the data flows that feed them. Prototypes that prove value in weeks, not slideware.
- Engineer the delivery system. Own CI/CD for what you build, extended to AI: prompt and context versioning, eval suites that run in the pipeline and gate releases.
- Operate (Day 2). Own what happens after launch — eval harnesses, groundedness and retrieval metrics, observability and tracing, cost and latency management, drift, and the handoff that lets the client’s team run it without you.
- Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, CDOs, and business leadership. Quantify impact in their terms. Support pre‑sales scoping and proposal work when the deal calls for it. What We’re Looking For Engineering foundation
- 8–10+ years across ML, data, and software engine