Senior AI-Native Forward Deployed Engineer | Remote | Long Term | C2C
- Jersey City, United States
Posted 17 hours ago
About the role
Job Description
Role: Senior AI-Native Forward Deployed Engineer
Duration: Long Term
Location: Remote- EST/ CST
Travel: Expect [25–50]% travel to customer sites
Consultant and builder: embed with enterprise customers to ship AI-native software, and advise their product engineering organization on building the same way.
About the Role
Software engineering is undergoing the biggest transformation in its history. We believe the future belongs to engineers who treat AI as a teammate, orchestrate fleets of agents, and deliver business outcomes at a speed that was not previously possible.
As a Senior AI-Native Forward Deployed Engineer, you will embed with enterprise customers to prototype rapidly, deploy production-grade AI systems, and help define what enterprise engineering looks like in the age of AI. This is a senior, consultative role. You will act as the technical consultant to the customer's engineering leadership on AI-native adoption strategy, guide their development teams through the change day to day, and bring the entire product engineering organization — not a pilot squad — to AI-native ways of working.
You are the lighthouse: you show the way, you flag the hazards, and you leave the team more capable than you found it.
What We Mean by AI-Native
Our engineers collaborate with AI agents across the whole software lifecycle. They use our own Astra AI-Native development platform alongside Claude Code, Cursor, GitHub Copilot, and emerging agentic tooling to accelerate delivery while holding a high bar for engineering quality. AI-native is not a tool choice — it is a change in how work is decomposed, reviewed, tested, and shipped.
Our Engineering Principles
AI first
Customer obsessed
Prototype fast, production faster
Humans + AI beats humans or AI alone
Continuous learning
Build once, reuse everywhere
Engineering excellence matters
Advise while you build
Key Responsibilities
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.
Turn 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.
Consulting Mandate: Moving the Whole Product Engineering Organization
Moving the whole product engineering organization to AI-native ways of working is a core deliverable of this role, not a side activity. You will own the engagement plan and the outcome.
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.
What Success Looks Like in Year One
First 90 days: adoption assessment complete, roadmap agreed with engineering leadership, first production AI-native workload shipped.
Six months: every product engineering team is working to the agreed AI-native baseline; standards and golden paths are in use on live work.
Twelve months: measurable delivery improvement against baseline metrics, and internal champions sustaining the practice without you.
Required Qualifications
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.
Preferred Qualifications
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
Signals We Look For
You think AI-first: you use AI agents as engineering teammates by default.
You build at high velocity: you prototype in hours and productionize in weeks.
You are fluent in modern AI engineering: agents, LLMs, evaluation, and the tooling around them.
You love solving customer problems: you translate ambiguity into elegant, shipped solutions.
You learn relentlessly: new model capabilities are an opportunity, not a disruption.
You think like an owner: you measure success through customer outcomes, not activity.
You elevate everyone around you: you advise, mentor, and contribute reusable accelerators.
You drive adoption at scale: you consult, advise, and influence without authority to move whole engineering organizations.
Join Us
If you are excited by frontier AI and eager to help define how enterprise software will be built over the next decade, we would love to meet you. Come help us build the future — one customer, one agent, and one breakthrough at a time.
To apply, send your resume and a short note about an AI-native workflow you have built or helped a team adopt to [application link or email].
Additional Information
All your information will be kept confidential according to EEO guidelines.