Role Description
AI Engineer (Forward Deployed)
SF/NYC / Travel / $200k-$350k + early stage equity
My client is looking for a Forward Deployed Engineer to sit at the intersection of engineering and customer delivery, building production AI systems directly with enterprise clients. You'll spend your time in customer environments, not just shipping code from behind a product spec, turning ambiguous real-world problems into working software that customers actually use.
Day to day, that means building and deploying LLM-powered and agentic systems, retrieval-augmented applications, and getting GenAI features from prototype into reliable production use. You'll own the evaluation and reliability side too, not just the initial build, so what you ship keeps working after you leave the room.
Strong fit for engineers coming from client-facing, applied-AI or consulting-led backgrounds, particularly those with experience at top-tier strategy or technical consulting firms. A strong CS degree is expected.
What My Client Is Looking For:
- 3+ years of industry experience
- Comfortable operating with ambiguity and directly in front of customers
- Strong Python and/or TypeScript skills in production environments
- Track record deploying data-heavy or AI-enabled applications
- Able to communicate clearly across technical and non-technical audiences
- Open to travel, roughly 20 percent
- Wants the pace and ownership of a startup environment
Nice to Have:
- Prior forward-deployed or customer-facing engineering role
- Hands-on production experience with ML, LLMs, or agentic systems
- Frontend experience, React preferred
About Forward Deployed Engineering
Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.
Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.
Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.
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