Note: The job is a remote job and is open to candidates in USA. The company is seeking a Senior AI-Native Forward Deployed Engineer to embed with enterprise customers as a hands-on technical consultant and builder. The role focuses on delivering production-grade AI applications, advising engineering leadership on AI-native adoption, and helping entire product engineering organizations adopt new tools, practices, standards, and measurable workflows.
Responsibilities
- Embed with enterprise customer teams as a hands-on senior engineer and trusted technical advisor
- Build AI-native applications and agentic workflows, including multi-agent systems, MCP integrations, and RAG pipelines
- Prototype in hours, then productionize what works — with the evaluation, observability, and CI/CD rigor production demands
- Turn one customer's innovation into a reusable capability the rest of our customers can adopt
- Advise engineering leadership on AI-native adoption strategy, tooling selection, and rollout sequencing
- Assess the customer's current development practices and produce a prioritized adoption roadmap with measurable outcomes
- Define the standards that make AI-assisted development safe: code review norms, prompt and context management, testing and evaluation, security and IP guardrails
- Navigate resistance and organizational inertia; build coalitions with staff engineers, architects, and delivery managers
- Assess capability gaps across engineers, QA, architects, and engineering managers, and define a role-based adoption plan for each group
- Work shoulder-to-shoulder with teams on their real backlog — pairing, design reviews, live build-alongs, and office hours — rather than classroom exercises
- Set an agreed baseline of AI-native fluency for every engineer, then advise team leads on closing the gap to it
- Identify and mentor internal champions who can sustain the practice after you rotate off
- Leave behind playbooks, prompt and context libraries, reference implementations, and golden-path templates in the customer's own repositories
- Measure adoption with agreed metrics — cycle time, review throughput, defect escape rate, tool usage depth — and report to leadership on a regular cadence
Skills
- 8+ years building and shipping production software, with recent hands-on delivery experience
- Demonstrated use of AI coding agents as part of your daily workflow — Claude Code, Cursor, GitHub Copilot, or equivalent
- Practical experience with LLM application patterns: prompting and context engineering, RAG, tool use, evaluation, and observability
- Strong proficiency in at least one of Python, TypeScript, C#, Java, or Node, and comfort reading the others
- Production experience on at least one major cloud (Azure, AWS, or Google Cloud) with containers and CI/CD
- A track record of advising and influencing engineering teams — you can point to people and teams who work differently because of you
- Consulting-grade communication: you can hold a room of skeptical senior engineers and a room of executives on the same day
- Willingness to travel to customer sites as the engagement requires
- Experience with agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or the OpenAI Agents SDK
- Experience building MCP servers or integrations
- Prior consulting, professional services, or forward-deployed engineering experience in an enterprise environment
- Experience driving a developer-productivity, platform-adoption, or DevEx transformation across an organization
- Familiarity with enterprise constraints on AI: data residency, IP and licensing, secure SDLC, and model governance
Benefits
- Remote work arrangement in the East/Central USA
Company Overview
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