Full Stack Software Engineer - AI

Position: Full Stack Software Engineer - AI Applications
Who You Are

An agent orchestrator, not a typist. You don't want to be a faster keyboard. You want to be a manager of agents - handing off the heavy lifting, reviewing the output, and keeping your hands on the architecture. The IDE is a cockpit for orchestration, not a text editor.

Productively lazy. Your dream workflow: describe the requirement, let the agent build it, verify, ship, next. You automate anything a human shouldn't be doing twice. Your biggest bottleneck should be deciding what to build - while AI executes the how.

Fundamentals first. Data structures, algorithms, distributed systems, networking - you understand the machine, not just the library that wraps it. When a framework breaks, you fix it. When AI gives you the wrong answer, you catch it. Orchestrating agents only works if you can tell good output from garbage.

First-principles thinker with vision. You break problems to their core, question the assumptions, and rebuild. A software engineer's job is to architect solutions, not wrestle with syntax. You don't copy an architecture because "that's how it's done" - you ask why and decide if there's a better way.

High agency. You don't wait for perfect specs or permission. You find a path, propose it, and move. Large organizations have walls; you figure out which ones to go through, around, or remove - and you do it constructively.

Bias for action. Requirements will be messy and priorities will shift. You ship v1, learn, and iterate instead of living in design review.

I'm looking for a "lazy" engineer. Not lazy about outcomes - lazy about effort that a machine should be doing. If hand-typing boilerplate makes you physically ill, if you'd rather describe a feature to an agent and move on to the next hard problem, you're who I want. You'll be a Full Stack Engineer who uses AI to build - and builds with AI.

Two things at once: you ship real applications faster by treating LLMs as a team of junior developers you manage, and you build the AI use cases themselves - agents, RAG pipelines, LLM-powered products that work 'll work across the stack - cloud, backend, AI agents, frontend - and own what you build. We're building the Universal Electric Vehicle at Ford, doing things differently.

If you already live in 2030, keep reading.

What You'll Do

* Orchestrate agents to build. Use AI as your default way of working - agents do the heavy lifting, you direct, review, and harden. You set the bar for how the team builds with AI.

* Build the AI use cases. Design and ship AI-powered applications and agents - RAG pipelines, LLM integrations, agentic workflows. Understand what's happening under the hood and make it work in production, at scale.

* Ship cloud-native systems on GCP. Design, deploy, operate. You own the architecture, the infrastructure-as-code, and the CI/CD pipeline. No throwing code over the wall.

* Connect systems that don't want to be connected. Build the integration layer across enterprise SaaS platforms. Expect messy APIs, legacy constraints, and creative problem-solving.

* Automate what shouldn't be manual. If a human repeats it, you write the code - or point an agent at it - to stop it.

* Make other engineers faster. Build the tools, agent workflows, and guardrails that remove friction. Developer productivity is a multiplier.

What You'll Do

* Orchestrate agents to build. Use AI as your default way of working - agents do the heavy lifting, you direct, review, and harden. You set the bar for how the team builds with AI.

* Build the AI use cases. Design and ship AI-powered applications and agents - RAG pipelines, LLM integrations, agentic workflows. Understand what's happening under the hood and make it work in production, at scale.

* Ship cloud-native systems on GCP. Design, deploy, operate. You own the architecture, the infrastructure-as-code, and the CI/CD pipeline. No throwing code over the wall.

* Connect systems that don't want to be connected. Build the integration layer across enterprise SaaS platforms. Expect messy APIs, legacy constraints, and creative problem-solving.

* Automate what shouldn't be manual. If a human repeats it, you write the code - or point an agent at it - to stop it.

* Make other engineers faster. Build the tools, agent workflows, and guardrails that remove friction. Developer productivity is a multiplier.