Forward Deployment Engineer

About the role

The AI Transformation Architect team defines and delivers high-impact Agentic AI solutions for strategic customers. This role translates complex business problems into validated solution designs and acts as the trusted technical advisor during presales. The position bridges customer needs with product direction while advancing enterprise AI adoption.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

Complex business problems are uncovered through deep discovery with customer stakeholders and operational leaders, and translated into clear Agentic AI solution designs.

Enterprise infrastructure constraints such as cloud, on-prem, and data pipelines are navigated to scope integration feasibility, guiding customers through Agentic AI evaluation and prototyping.

Requirements

4+ years of experience in high-impact, customer-facing technical roles such as AI Architect, Technical Consulting, Sales Engineering, Solutions Architecture, or technical consulting for SaaS or AI products.

Demonstrated expertise in the modern AI/ML stack, including LLMs, Generative AI concepts, vector databases, and agent orchestration frameworks, with hands-on experience in Python, Go, JS/TS, and LLM frameworks.

Ability to simplify technical complexity and communicate trade-offs and value propositions to both engineering teams and non-technical executives.

Thrive in ambiguity, take ownership of outcomes, and deliver measurable results for customers, often under tight timelines.

Contribute to a dynamic team culture centered on high-growth innovation, collaboration, customer success, and enjoyment.

Practical notes

This role is customer-facing and requires engagement with strategic accounts in London. The position involves travel within the UK and may require participation in AI envisioning workshops and Proofs of Concept sessions. Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

AI tools are central to how this role operates and how employees drive impact across the organization.

The position sits at the intersection of sales, engineering, and product, enabling continuous feedback loops to shape platform evolution.

Work focuses on real-time customer problem resolution across voice and digital channels using agentic AI and automation.

The role emphasizes scrappy, curious, optimistic, persistent, and empathetic collaboration within a high-growth environment.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.