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

vibehackers; United States; Remote; USD 180,000 - 230,000; Full time

Job summary

Vibehackers seeks a Senior AI-Native Forward Deployed Engineer to prototype, deploy, and operationalize AI-native software for enterprise customers. You will be hands-on and advisory, guiding engineering leadership toward AI-native adoption.

This remote role (EST/CST timezone) requires 25–50% travel and a track record delivering production AI systems with multi-agent workflows, RAG pipelines, and dependable CI/CD. You will mentor teams and turn innovations into reusable capabilities.

Qualifications

• 8+ years building and shipping production software.
• Practical experience with AI coding agents in daily workflow.
• LLM prompting, context engineering, and observability.
• Strong proficiency in Python/TypeScript/C#/Java and at least one major cloud.
• Experience advising engineering leadership and working with enterprise customers.

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 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).

Skills

8+ years production software

AI coding agents

LLM patterns

Programming: Python/TypeScript/C#/Java

Cloud: Azure/AWS/GCP

Consulting communication

Willingness to travel

Tools

Claude Code

Cursor

GitHub Copilot

LangGraph

CrewAI

AutoGen

Semantic Kernel

OpenAI Agents SDK

Job description

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 C2C

Key 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 Copilot
• Agent frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK
• Languages: Python, TypeScript, C#, Java, Go, Node

Logistics

• Remote role aligned to EST/CST timezone
• Travel expectation: 25–50% to customer sites

Skills

Consulting Technical Leadership Mentoring Customer-facing Prototyping System Integration Observability CI/CD Testing Prompt/Context Engineering Evaluation & Metrics Architecture Stakeholder Management Change Management Communication