Forward Deployed Engineer (FDE) — AI / Agentic Software
Carnaby Fox
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Benefits offered by this job
Job summary
Carnaby Fox is seeking a Forward Deployed Engineer (FDE) to work across software, AI, product, and operations. You will embed with portfolio companies, build AI-powered production systems, and own the delivery lifecycle from design to deployment.
Ideal candidates have 3–6 years of full‑stack or forward deployed engineering, with experience in TypeScript/Python and Go, plus expertise in AI tooling, LLMs, and agentic workflows. Hybrid work and frequent travel are required.
Qualifications
- Experience building production AI systems (not just demos).
- Ability to own end-to-end technical delivery from backend to frontend and data layer.
- Experience with LLMs, agentic workflows, and AI-powered automation.
Responsibilities
- Embed with portfolio companies to understand operating models and priorities.
- Design and build production AI solutions, including integrations and agentic workflows.
- Operate deployed systems in production and tune performance.
Skills
Tools
Job description
We’re looking for Forward Deployed Engineers who can operate at the intersection of software engineering, AI, product, and business operations.
This is a highly hands‑on, Palantir-style FDE opportunity within a software holding company. You will work directly with portfolio companies, understand their operational challenges, identify high‑impact opportunities for AI and agentic automation, and then build, deploy, operate, and improve production systems end‑to‑end.
This is not a presentation‑heavy solutions role. You will be expected to write production code, work directly with operators and executives, and take ownership of measurable outcomes.
- Work directly with portfolio companies to understand their operating models, workflows, and business priorities.
- Build relationships with GMs and operational leaders and quickly earn their trust.
- Identify the 2–3 highest‑leverage workflows where agentic software can create meaningful impact.
- Design and build the initial “wedge” for each deployment.
- Develop integrations, prompts, evaluations, UI surfaces, and agentic workflows.
- Launch the first production agentic loop using the organization’s core platform.
- Own the entire technical delivery cycle rather than handing work between specialized teams.
- Monitor and tune deployed systems in production.
- Adjust autonomy levels and gating based on real‑world performance.
- Track outcomes and continuously improve deployed workflows.
- Transition stable solutions to portfolio operations teams when appropriate.
- Serve as the technical face of the organization to portfolio company GMs.
- Translate ambiguous operational challenges into clearly scoped technical solutions.
- Set expectations, communicate trade‑offs, and ensure stakeholders understand the value created by the system.
- Bring lessons from deployments back to HQ.
- Identify recurring patterns across companies and verticals.
- Codify successful approaches into reusable playbooks.
- Help accelerate future deployments by turning individual solutions into repeatable systems.
You should be comfortable owning a production system from backend through frontend and data layer.
- TypeScript or Python for backend development.
- Ability to independently own end‑to‑end technical delivery.
- Experience programming with Go and/or TypeScript.
- Experience working with modern AI development tooling and coding agents.
- Strong understanding of LLMs / foundation models and agentic workflows.
A major requirement is experience building real production AI systems, not simply experimenting with AI or creating demonstrations.
Strong candidates will have experience with:
- Production LLM applications
- Agentic workflows
- AI‑powered automation
- Evaluation frameworks / evals
- Autonomy gating
- Outcome tracking
- AI developer tools and coding agents
- Integrations and workflow automation
- Production deployment and post‑launch iteration
Technical ability alone isn't enough for this role.
You should be able to:
- Understand how a business operates.
- Read and interpret a P&L.
- Translate business problems into technical opportunities.
- Determine where AI can create the highest leverage.
- Work effectively with non‑technical executives.
- Navigate ambiguity without requiring highly prescriptive requirements.
- Balance technical quality with business outcomes.
The strongest candidates may come from backgrounds such as:
- Senior full‑stack engineer with direct customer exposure
- Product engineer who has shipped production AI systems
- Generalist engineer capable of owning an entire technical solution
- Early‑stage engineer
- Engineers who have operated with significant autonomy in high‑growth environments
- Experience working with PE‑backed or portfolio companies
- Consulting environments
- Vertical SaaS
- M&A integration or similar operational transformation work
You should demonstrate:
- Strong executive presence.
- Polished communication with C‑suite executives and GMs.
- Ability to explain complex technical concepts clearly.
- Ability to build trust quickly with business operators.
- Comfort working in ambiguous, rapidly changing environments.
- Ability to independently drive projects from problem discovery through deployment and measurable outcomes.
- 3–6 years of relevant experience in full‑stack, customer‑facing software/product engineering or forward deployed engineering
- Production experience with LLMs / agentic software
- Strong TypeScript or Python
- Experience with AI development tools and coding agents
- Customer‑facing / embedded engineering experience
- Strong business and operator instincts
- Ability to work directly with executives and GMs
- Strong communication and executive presence
- Willingness to travel frequently to portfolio company locations
- Ability to work in a hybrid environment
- Early‑stage startup engineers
- Product Engineers with strong AI experience
- Engineers from high‑growth technology companies
- Customer‑facing engineers who have owned production deployments
Languages: TypeScript, Python, Go
Frontend: React
AI: LLMs, Foundation Models, Agentic Workflows, Evals
Development: AI-native development tools and coding agents
- Your experience is limited to solutions engineering demos or prototypes without production ownership
- You have primarily worked as a backend-, frontend-, or mobile-only specialist and cannot own full‑stack delivery
- You have not built or coded with modern AI development tools
- Your experience consists primarily of short consecutive stints
- You have not shipped production AI/agentic systems to real users
- You prefer narrowly defined requirements and do not enjoy ambiguous business problems
- Competitive Equity
- Hybrid work model
- Frequent exposure to portfolio companies and high‑impact deployments
- Visa transfer and relocation opportunities available
This is an opportunity to work on high‑impact AI deployments where engineering directly changes how businesses operate.
Instead of building isolated features, you will repeatedly:
You’ll work closely with business operators, ship production AI systems, learn from each deployment, and help turn those learnings into reusable solutions across an entire portfolio.
Candidates should be comfortable with a hybrid schedule and 2–3 days per week of domestic travel to portfolio company locations.
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