Senior AI-Native Forward Deployed Engineer | Remote | Long Term | C2C

Explicitly requires vibe-coding: uses Cursor, Claude Code, GitHub Copilot and agentic tooling for rapid prototyping and production. About the Role Embed with enterprise customers as a senior technical consultant to prototype, deploy, and operationalize AI-native software and agentic workflows, while driving organization-wide adoption and reusable capabilities. The role combines hands-on engineering, advisory work with leadership, and mentorship to move entire product engineering organizations to AI-native ways of working. Job Description Role Senior AI-Native Forward Deployed Engineer embedding with enterprise customers to prototype rapidly, deploy production-grade AI systems, and advise engineering leadership on AI-native adoption strategy. The role is hands-on and consultative: you will deliver code, define standards, and help entire product engineering organizations adopt AI-native practices. Duration & Location Long term, remote (EST/CST timezone)Travel to customer sites expected: 25–50%Contract type indicated as C2CKey Responsibilities Embed with customer engineering 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 and productionize with evaluation, observability, and CI/CD rigor.Turn customer innovations into reusable capabilities and golden-path templates for other customers.Advise engineering leadership on adoption strategy, tooling selection, and rollout sequencing.Assess development practices, produce prioritized adoption roadmaps with measurable outcomes, and define safe AI-assisted development standards (code review norms, prompt/context management, testing, security/IP guardrails).Work shoulder-to-shoulder on real backlogs: pairing, design reviews, build-alongs, and office hours.Measure and report adoption using agreed metrics (cycle time, review throughput, defect escape rate, tool usage depth).What Success Looks Like (Year One) First 90 days: adoption assessment complete, roadmap agreed, first production AI-native workload shipped.Six months: every product engineering team working to the agreed AI-native baseline; standards and golden paths in use on live work.Twelve months: measurable delivery improvement and internal champions sustaining the practice.Required Qualifications 8+ years building and shipping production software with recent hands-on delivery experience.Demonstrated use of AI coding agents in daily workflow (e.g., Claude Code, Cursor, GitHub Copilot).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; ability to read others.Production experience on at least one major cloud (Azure, AWS, Google Cloud) with containers and CI/CD.Track record advising and influencing engineering teams; consulting-grade communication and stakeholder management.Willingness to travel to customer sites as required.Preferred Qualifications Experience with agent frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK).Experience building MCP servers or integrations.Prior consulting, professional services, or forward-deployed engineering experience in enterprise environments.Experience driving developer-productivity, platform-adoption, or DevEx transformations.Familiarity with enterprise AI constraints: data residency, IP/licensing, secure SDLC, model governance.Technologies & Tools AI development tools: Astra, Claude Code, Cursor, GitHub CopilotAgent frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDKLanguages: Python, TypeScript, C#, Java, Go, NodeLogistics Remote role aligned to EST/CST timezoneTravel expectation: 25–50% to customer sitesSkills Consulting Technical Leadership Mentoring Customer-facing Prototyping System Integration Observability CI/CD Testing Prompt/Context Engineering Evaluation & Metrics Architecture Stakeholder Management Change Management Communication