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

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 p…