Forward Deployed Engineer
Posted 15 May 2026
Advertised as “Forward Deployed Engineer ”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 6+ yrs
- Management level
- Individual contributor
- Contract
- Full-time
No salary listed for this role.
Field of studyComputer Science, Data Science, Engineering (or related)
Study levelBachelor's or higher
Are you looking to thrive in a stimulating work environment?
Join Levio, a leader in digital transformation, and take your career to the next level. You will work alongside high-caliber professionals on ambitious, large-scale technology projects, directly embedded in our clients’ environments. At Levio, we value expertise, curiosity, and continuous improvement — and we give you the space to grow.
About the Role
We embed engineers directly inside enterprise clients to find, build, and ship AI and agentic systems that reach production and stay there. A Forward Deployed Engineer - FDE runs discovery with the customer's own stakeholders, scopes the use cases worth building, then writes and deploys the working system — in the customer's environment, against their data, under their security and governance constraints.
This is a build-with-the-customer role, not an advisory one. You are the person in the codebase and in the room, closing the gap between an ambiguous "we should use AI" and a governed system operators actually trust and use. You own outcomes through real production usage — you do not hand off a slide deck or a prototype and move on.
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Why Join Levio?
- Work on complex, high impact digital transformation projects
- Collaborate with experienced, multidisciplinary teams
- Continuously develop your technical and professional expertise
- Enjoy flexibility, autonomy, and a strong people first culture
- Be part of an organization that values diversity, inclusion, and innovation
Role and Responsibilities
- Run discovery with the customer's business stakeholders and turn open-ended "we should use AI" asks into scoped use cases, ranked by ROI, data readiness, and technical feasibility.
- Build and ship production GenAI and agentic systems end to end — retrieval (RAG), agent orchestration and tool/MCP integration, evaluation harnesses, and the surrounding application and data plumbing.
- Deploy inside the customer's environment and constraints: their cloud, their security posture, their data governance — not a sandbox you control.
- Design for trust — keep business-critical logic (pricing, rules, calculations) in deterministic code paths, build evaluation and human-in-the-loop review, and know when model output is safe to put in front of operators.
- Embed and enable: document the system, run walkthroughs, and lift adoption — while owning the system through real production use, not until the demo.
- Present architecture, cost, and delivery trade-offs directly to non-technical decision-makers and keep them bought in.