Role profile · Engineering
Deployed / Solutions Engineer
A type of work, not one opening. Join it from your career profile and you’re matched to Deployed / Solutions Engineer roles as they open. No match is guaranteed, and no company sees you until you say yes.
Titles it covers
Forward Deployed Engineer. FDE, Forward Deployment Engineer, Deployed Engineer, Agent Deployment Engineer, Customer Engineer, Implementation Engineer, Field Engineer
Solutions Architect / Engineer. Solutions Architect, Solutions Engineer, Applied AI Architect, Delivery Solutions Architect, Outcomes Architect
Deployment Strategist / Engagement Manager. Deployment Strategist, Agent Development Strategist, Engagement Manager, AI Success Manager, Technical Deployment Lead
What strong looks like
Has shipped inside a customer's environment, not just a demo. Names a customer whose data or stack was not what the contract said and what they did about it. Comfortable being the person in the room when it fails. Can say what they built versus what they configured. Knows when to say no to a customer.
Core must-haves
- Deployed something into a customer's environment that went to production, not a demo
- Communication with executives and engineers in the same week: explains, listens, and writes it down
- Technical depth to build the integration themselves: a backend language plus APIs and system design
- Technical plus commercial blend: discovery, scoping, proof of concept, and knowing when to say no
- Has built with LLMs or agents in a customer context, or can reason about where they fail
- Comfortable with enterprise cycles: procurement, security review, multi-threaded stakeholders
Across every profile: ownership in ambiguity; communication that survives a follow-up; ai-native practice; verifiable outcomes.
Good to have
- A vertical domain: legal, healthcare, financial services, robotics
- Travel and on-site tolerance, captured in constraints, never assumed
- Prior sales engineering, consulting, or customer success experience
- Founding or 0-to-1 experience
- Data engineering competence for messy customer data
- Program or engagement management experience
Seniority
Open from recent graduates to 15 or more years. We read level from what you have owned, not from years or brand. What the evidence looks like at each stage:
New grad to 2 years. Customer exposure counts even from student clubs or internships (one large employer says so explicitly). Judged on how they handle the Friday-data scenario and whether they can explain a technical thing to a non-technical person.
2 to 5 years. Has run at least one deployment end to end in a customer environment, has an escalation story.
5 to 10 years. Owns multiple engagements, cuts scope with the customer, moves their own product team, has walked away from a bad fit.
10 to 15+ years. Sets the engagement model others run, shapes what the product team builds from field evidence, leads the largest accounts.
What to bring
- Two or three pieces of real work that fit the must-haves: how it came about, what was yours, what was reused, how you did it, what broke and how you fixed it.
- How you work with AI agents. Where does AI sit in how you build and deploy for customers today, prototyping, integration code, customer docs, or somewhere else?
- What you’re looking for, and your constraints, which stay private.