AI-Native Forward Deployed Engineer

• Deliver with the customer
• 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.
• Turning one customer’s innovation into a reusable capability the rest of our customers can adopt.

Consult on AI-native adoption:

• 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.

• 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.

Technologies You May Work With:

• AI development tools: Claude Code, Cursor, GitHub Copilot, Astra
• Models: Anthropic Claude, OpenAI, Gemini
• Agent frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK
• AI infrastructure: MCP, RAG, vector databases, evaluation, observability
• Languages: Python, TypeScript, C#, Java, Go
• Cloud: Azure, AWS, Google Cloud
• Platform: Kubernetes, Docker, CI/CD, Infrastructure as Code