Full Stack Software Engineer - AI Applications

Ford Ford · Auto · Dearborn, MI +2 · Enterprise Technology

This role focuses on building and shipping AI-powered applications and agents, specifically RAG pipelines, LLM integrations, and agentic workflows, within an enterprise context. The engineer will orchestrate agents for development, connect disparate systems, and automate manual processes, while also owning the cloud-native infrastructure on GCP and CI/CD pipelines. The emphasis is on practical application and production deployment of AI within a large organization.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Automate what shouldn't be manual. If a human repeats it, you write the code — or point an agent at it — to stop it.

Skills

Required

  • Python
  • JavaScript / TypeScript
  • React
  • Node.js
  • AI engineering
  • LLMs
  • embeddings
  • vector stores
  • RAG
  • agents
  • evaluation pipelines
  • GCP
  • Cloud Run
  • Cloud Functions
  • GKE
  • BigQuery
  • Cloud Storage
  • Databases
  • APIs & microservices
  • RESTful services
  • Git
  • IaC
  • CI/CD
  • Terraform
  • Cloud Build
  • BS in Computer Science, Electrical Engineering, or a related field
  • 2+ years building and deploying full-stack applications in production
  • 2+ years on cloud-native platforms (GCP preferred)

Nice to have

  • Front-end strength
  • fine-tuning
  • eval work
  • MS in Computer Science, Electrical Engineering, or a related field

What the JD emphasized

  • agent orchestrator
  • AI applications
  • production at scale
  • enterprise SaaS platforms
  • developer productivity
  • building and deploying full-stack applications in production
  • cloud-native platforms
  • building AI agents or applications

Other signals

  • building AI applications
  • production at scale
  • cloud-native systems on GCP
  • enterprise SaaS platforms
  • developer productivity