Forward Deployed Engineer
Strong across client-accountable consulting, production engineering, and team delivery. Ready to enter an unfamiliar client environment, earn trust, and own the path from ambiguity to measurable business value.
Practitioner-led screening
How we screen AI-native specialists.
Strong AI engineering is not enough to make someone a Forward Deployed Engineer. We look for practitioners who can enter an unfamiliar client environment, earn stakeholder trust, turn ambiguous business needs into working systems, and deliver through a team. They must also understand, read, and debug the code AI helps them produce.
FDE screening dimensions
Client consulting, engineering ownership, team delivery
Conducted by
FDE evaluations are led by principal-level practitioners
Audience
For CTOs and engineering leaders evaluating Elios specialists
WHAT WE SCREEN FOR
Consultant, engineer, teammate.
The FDE bar requires all three. Technical strength cannot compensate for weak client judgment. Polished communication cannot compensate for shallow engineering ownership. Individual speed cannot come at the expense of the team.
01 / CONSULTANT
Client-accountable consulting
FDEs must have delivered for external clients who held them accountable for the outcome. They move toward difficult stakeholders, gather requirements before proposing architecture, and connect technical decisions to business results.
What we verify
Verified client-facing delivery. Business acumen. Clear communication with technical and non-technical stakeholders. Evidence that they can earn trust when the environment is ambiguous or resistant to change.
02 / ENGINEER
Production engineering ownership
FDEs take AI systems from proof of concept to production and remain accountable when they break. AI accelerates their work. It does not replace their ability to read code, isolate failures, debug independently, or explain what they personally built.
What we verify
Owned systems with measurable business outcomes. Code-level debugging ability. Sound evaluation and observability practices. Concrete, technically detailed accounts of decisions, failures, and fixes.
03 / TEAMMATE
Team delivery and AI fluency
FDEs work inside small, cross-functional client teams. They make architects, product owners, engineers, and skeptical stakeholders more effective. They use current AI tools with judgment while staying flexible enough to contribute across the stack and through the team.
What we verify
Collaborative delivery in mixed-discipline teams. Respect for reluctant stakeholders. Current agentic engineering fluency. A willingness to handle the unglamorous work required to move the client forward.
BEFORE THE CONVERSATION
It starts inside Elios Insights.
Long before a candidate reaches a screening conversation, the search begins in Elios Insights, our proprietary workforce platform. Background and resume review is not a one-off task. It runs against a curated database, with structured requirements and a tracked funnel for every role.
200,000+
Specialists in our network, searchable by role, skills, and location
Prism
AI-assisted search turns a plain-language brief into a ranked shortlist
Curated
Every shortlisted profile is reviewed by an Elios team member with a documented reason
eliosinsights.com · Prism Search
Search in plain language. Prism translates a brief like "AI Engineer in Orlando, Python, TensorFlow, PyTorch" into a ranked shortlist across the full database, with optional filters for role, location, experience, and skills.
eliosinsights.com · Talent Request
A tracked funnel for every role. Each role becomes a structured Talent Request with defined requirements and skills. Candidates move through a tracked funnel, from sourcing through screening to close, so nothing is informal and nothing is lost.
eliosinsights.com · Curated Profile
ELIOS CURATED
A reviewed profile, not a scraped one.
Every shortlisted candidate is curated by an Elios team member before it ever reaches you. The profile pulls together full career history, tenure signals, education, and an AI-generated summary.
Crucially, each one carries a documented reason for curation, attributed to the Elios team member who made the call. The resume review is on the record, not a gut feeling.
Tenure signalsFull work historyAI summaryReason for curation
SCREENING PROCESS
Evidence before labels.
We classify candidates by the work they have owned and the environments where they have delivered. We keep our evaluation prompts private, but we are transparent about the evidence required to earn the FDE label.
- 01
Resume and portfolio review
Run inside Elios Insights against our curated database. For an FDE screen, the work history must show client-accountable delivery: a role where an external client could hold the candidate responsible for the outcome. Without that evidence, we evaluate the candidate for a different engineering track.
- 02
Experience verification
We review relevant professional and technical evidence to verify consulting delivery, production ownership, and current AI fluency. We look for a consistent record of what the candidate personally owned, how the work reached production, and how success was measured.
- 03
Principal-level FDE evaluation
For the FDE track, principal-level practitioners evaluate how candidates navigate clients, translate business needs, work through teams, and own production systems. FDE candidates must explain their work with enough specificity to demonstrate real judgment, including how they read and debug AI-generated code when the model cannot solve the problem. Other specialist tracks use evaluation aligned to the work they will perform.
WHAT WE SCREEN OUT
Not everyone with "AI" on their resume meets the bar.
We actively filter for patterns that signal a candidate will struggle in a client-facing deployment role.
Client deflection.
FDEs do not avoid skeptical clients or hand difficult stakeholders to someone else. They listen, find the source of resistance, and create a path to a verifiable win.
Solo-operator behavior.
The role requires delivery through a client team. A preference for working alone cannot become resistance to collaboration, shared ownership, or colleagues who are still adapting to AI.
AI-dependent engineering.
AI-native engineers use models to amplify their judgment. They do not outsource it. An FDE must be able to inspect a diff, read unfamiliar code, isolate a failure, and debug when the model cannot. AI can write the code. The engineer still owns it.
Architecture before understanding.
Candidates who jump to models and pipelines before understanding the client, the users, the constraints, and the business goal are solving the wrong problem faster.
Technical metrics without business value.
Uptime and accuracy matter, but they are not the outcome. FDEs must connect system performance to the measures the client uses to judge success, such as revenue, conversion, adoption, cost, or resolution.
Vague ownership.
Real practitioners can explain the system, the decision, the failure, and the fix. Technology lists and borrowed team credit do not demonstrate that the candidate can own the work in a client environment.
PROOF POINT
What clears the FDE bar.
A recent candidate who earned the Forward Deployed Engineer classification demonstrated the following profile.
The bar
Consultant, engineer, teammate.
Over a decade of production software engineering across enterprise consulting engagements, with three-plus years focused on LLM-powered systems. They had architected a multi-agent platform using the orchestrator-workers pattern with MCP integrations, built hybrid RAG pipelines with measurable retrieval metrics, and shipped a merchant-facing AI product handling thousands of daily queries with 99.9% availability.
That level of technical depth is necessary, but it is not enough. To earn the FDE classification, a candidate must also explain decisions to non-technical stakeholders without losing precision, connect system performance to business outcomes, demonstrate client-accountable consulting experience, and make the delivery team more effective.
Strong AI engineering creates credibility. Consulting judgment and team delivery make an FDE.
ROLE CLASSIFICATION
A strong AI engineer is not automatically an FDE.
Every evaluation ends with a role-specific classification. We present candidates for the work their evidence supports instead of stretching one label across fundamentally different strengths.
Forward Deployed Strategist
Exceptional with clients, business framing, and stakeholder alignment, with lighter hands-on engineering depth. Best suited to strategy-led deployment work where translating between business and technical teams is central.
AI Engineer
Strong production and AI engineering skills without verified client-accountable consulting experience. Eligible for product and direct-enterprise engineering roles, but not presented as an FDE for consulting-led engagements.
Get the role right before the engagement starts.
Elios screens specialists against the work they will actually do. For FDEs, that means client-accountable consulting, production engineering ownership, and the ability to make the whole client team more effective.
Elios is part of the OpenAI Partner Network and Anthropic's Claude Partner Network. These are company-level relationships. Every candidate decision still comes from practitioner-led evaluation, not partner status.