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Forward Deployed Engineers embed directly with a strategic customer and own the full arc of an AI engagement: technical discovery, architecture, build, production deployment, and post-deployment support. This is not a pre-sales or solutions-engineering role. The expectation is shipped, production code running in the customer's environment, not demos.

The closest analogy is an embedded startup CTO. You sit inside the customer's team, make the hard architectural calls, and take frontier GenAI workflows all the way to production with a small, agile pod behind you rather than a large delivery org. You also carry a second job: turning what you learn in the field into reusable patterns, accelerators, and product feedback that scale across accounts. That "codify and feed back" loop is what separates this role from staff augmentation.

You will work across the full lifecycle, translating ambiguous business problems into AI solutions, leading a workstream end to end, and partnering with sales, product, and customer stakeholders for a seamless path from discovery to production.

What You'll Do

- Own end-to-end delivery. Guide strategic customers through discovery, design, build, and deployment of frontier GenAI applications, then support them in production. Scope the delivery work with engagement leadership and lead the workstream to completion.

- Build production applications on frontier platforms.** Ship real code directly inside customer systems — LLM workflows, agentic apps, tool-use integrations, RAG and multi-agent systems, and the supporting artifacts (MCP servers, sub-agents, agent skills) that take a solution to production.

- Own the architecture.** Lead design decisions for secure, scalable solutions aligned to both the customer's needs and platform best practices.

- Translate business problems into AI solutions.** Work directly with technical and non-technical stakeholders to turn ambiguous, evolving requirements into a working architecture and a shipped result.

- Provide white-glove deployment support** in enterprise environments, including production hardening, evaluation, and monitoring.

- Codify and feed back.** Identify repeatable deployment patterns and turn one-off client work into reusable accelerators, frameworks, and product feedback that scale impact across accounts and inform the roadmap.

- Build long-term customer relationships** and proactively surface new deployment opportunities across the engagement lifecycle.

Required Qualifications

- 3+ years** of software or data engineering experience, with demonstrated production delivery.

- 1+ year deploying GenAI** in client or production environments — not prototypes or internal experiments.

- 1+ year with a frontier platform:** Claude API, Claude Code, tool use, and extended thinking, or the equivalent on Google or OpenAI platforms.

- 1+ year leading workstreams** and translating business problems into AI solutions.

- Strong Python.** Additional languages (TypeScript, Java, SQL, and similar) a plus.

- Production LLM fluency: advanced prompt engineering, agent development, evaluation frameworks, and deployment at scale.

- High agency and comfort with ambiguity.** You navigate shifting scope and complex organizations without waiting to be told what to build.

- Willingness to travel ~50%**, depending on account and geography.

Preferred Qualifications

- MLOps / LLMOps practice:** evaluation frameworks, model monitoring, and prompt management in production.

- Former technical founders and SWEs with consulting experience are explicitly welcomed.

- Full-stack range across backend, frontend, and systems integration — production delivery that combines data pipelines, AI models, and user-facing interfaces.

- Familiarity with multiple AI APIs (Anthropic, OpenAI, Google) and modern AI coding tools.

- High-cooperation mindset across competing customer and internal priorities.

Compensation

**$134,500 – $180,000** base, commensurate with experience and level, plus incentives. FDE is positioned as a senior individual-contributor / hybrid role.