Ford Motor
From Ford Motor's careers page
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As a Senior DevOps Engineer, you will play a hands-on, technical role in enabling Product Development Office (PDO) teams.
Join the Global Dev Tools team within the Enterprise Platform Engineering and Operations (EPEO) organization to build and operate the cloud-native platform that powers Ford's Software Development Lifecycle (SDLC). As a Senior DevOps Engineer, you will play a hands-on, technical role in enabling Product Development Office (PDO) teams. You will design, build, and operate the infrastructure, CI/CD pipelines, and automation that let engineering teams ship reliably and often.
Your work will center around Ford's core tech stack, featuring Google Cloud Platform (GCP) and Azure for cloud infrastructure, and GitHub Enterprise with GitHub Actions and Jenkins for CI/CD. Infrastructure is fully codified with Terraform, with GitHub Actions Runner Controller (ARC) self-hosted runners deployed on Kubernetes (GKE) to power scalable, on-demand CI/CD. You'll support Actions workflows that build and deploy Java (Spring Boot), Go, and Python applications, orchestrate AI agents to automate operational toil, lead technical evaluations of emerging AI developer and infrastructure tools, and ensure all platform systems meet Ford's stringent performance, security, and scalability benchmarks.
Build the platform. Use Terraform to provision and manage GCP infrastructure — GKE clusters, VMs, storage buckets, networking, and other resources as the platform grows — with Config Sync driving GitOps-style deployment of the GitHub Actions Runner Controller (ARC) and other workloads onto GKE. Lay the foundation for expanding into additional GCP services (Cloud Run, Compute Engine, Cloud Functions, IAM) and Azure as platform needs evolve.
Core scripting, config, and version control. Write and maintain YAML for pipeline definitions, Kubernetes manifests, and GitOps configs; use Git and GitHub Enterprise fluently for branching, PR workflows, and repository management; script in Bash/Shell (in addition to Python/Go) to glue together automation, tooling, and day-to-day operational tasks.
Automate everything that shouldn't be manual. Infrastructure-as-code (Terraform), self-service pipelines, auto-remediation. If a human repeats it, write the code — or point an agent at it — to stop it.
Own CI/CD end to end. Design, develop, and optimize robust, automated GitHub Actions workflows and Jenkins pipelines that package, test, and deploy software platform utilities. Support workflows that build and deploy Java (Spring Boot), Go, and Python applications, letting engineers ship multiple times a day with confidence.
API & Backend Integration. Design, develop, and secure backend APIs and integrations using Java (Spring Boot), Go, or Python; integrate with enterprise API gateways (Apigee) using OAuth2 and JWT, and support API lifecycle management across platform tooling.
Run containers and orchestration in production. Build, secure, and operate container images with Docker and/or Podman, and run them at scale on Kubernetes (GKE) — including the parts that break: crash loops, resource contention, networking edge cases, and noisy-neighbor problems. Bring the operational maturity to diagnose and fix these fast, and to harden the platform against recurrence.
Engineer reliability and observability in. SLOs, monitoring, alerting that means something, and incident response that gets calmer every quarter. Leverage GCP BigQuery and Data Studio (Looker Studio) to build observability and platform-health dashboards that surface real signal to stakeholders. Make on-call boring.
Own incident response. Bring a strong troubleshooting mindset to production issues — diagnose root cause quickly under pressure, drive incidents to resolution, and turn each one into a lasting fix rather than a repeat page.
Secure the path. Bake security and compliance into the platform — secrets management, least privilege, supply-chain hygiene — so doing the right thing is the easy thing. Drive SonarQube integration across CI/CD pipelines to enforce code quality and security gates as part of the developer workflow, not an afterthought.
Orchestrate agents for ops. Use AI to draft IaC, generate tests, triage incidents, and summarize signal from noise. You direct, review, and harden — agents do the grunt work.
AI Integration & Engineering. Architect and build production-ready integrations, extensions, and custom utilities to embed AI-assisted tools (e.g., GitHub Copilot, Cursor AI, QODO) directly into Ford's engineering workflows.
AI/ML Product Experience. Apply hands-on knowledge of designing, developing, and implementing AI/ML products, along with practical experience using AI developer tools such as GitHub Copilot, JetBrains AI Assistant, Cursor AI, QODO, or open-source AI tooling, to continuously evaluate and improve the platform's AI capabilities.
Prompt Engineering & Automation. Act as the hands-on engineering lead for prompt design and LLM orchestration. Develop automated workflows to generate code, templates, and high-quality technical content across Ford's primary stacks.
Support AI workloads (bonus). Build the infrastructure that runs LLM and agentic systems in production — scaling, cost control, and the deployment patterns AI apps actually need.
Customer-focused, security-minded delivery. Approach platform and tooling decisions from the perspective of the engineering teams you serve, balancing usability and developer experience with Ford's security and compliance requirements.
Bridge platform engineering and developer experience. Communicate clearly and often with stakeholders across engineering, security, and product teams — translating platform capabilities into developer-facing value and surfacing developer pain points back into platform priorities.
Continuous improvement. Proactively identify gaps, inefficiencies, and risks in existing tooling, pipelines, and processes; propose and drive improvements that raise reliability, security, and developer velocity across the platform.
Extreme Programming (XP) & Agile Craftsmanship. Embody software craftsmanship by practicing test-driven development (TDD), pair programming, and continuous integration. Actively participate in Agile ceremonies, translating technical requirements into clean, maintainable code.
Technical Troubleshooting & Mentorship. Serve as the senior technical escalation point for global engineering teams, resolving complex build, security scanning, and deployment issues. Mentor junior engineers in AI-assisted development and CI/CD best practices.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a closely related technical field.
8 years of Hands-on DevOps/Platform Engineering experience
Strong programming ability in a real language — Python and/or Go preferred — with a track record of shipping production code
Proficiency authoring YAML for CI/CD pipelines, Kubernetes manifests, and configuration-as-code
Strong Git and GitHub workflow experience — branching strategies, pull requests, and repository management at scale
Solid scripting ability in Bash/Shell/python for automation and operational tooling
Deep, hands-on GCP experience (GKE, and ideally Cloud Run, Compute Engine, Cloud Functions, networking, IAM) at production scale
Strong Infrastructure-as-Code experience with Terraform, provisioning GCP resources broadly — compute, storage, networking, and GKE clusters
Hands-on CI/CD pipeline experience with GitHub Actions and/or Jenkins
Production experience containerizing applications (Docker or Podman) and operating them at scale on Kubernetes, including diagnosing and resolving cluster-level issues
Experience with GitOps tooling (e.g., Config Sync, ArgoCD, or Flux) for declarative, version-controlled infrastructure and workload deployment
Experience integrating and configuring SonarQube (or equivalent) within CI/CD pipelines for code quality and security scanning
API design and integration experience, including RESTful services and API gateway management (Apigee, OAuth2, JWT)
Hands-on experience with GCP BigQuery and Data Studio/Looker Studio for building observability and reporting dashboards
Observability chops — metrics, logs, tracing — and the judgment to find the needle, not just collect haystacks
Knowledge and hands-on experience designing, developing, and implementing AI/ML products, along with fluency in AI-assisted development tools (GitHub Copilot, JetBrains AI Assistant, Cursor AI, QODO, or open-source alternatives) and agent workflows, with clear judgment on where they help and where they don't
Working knowledge of SLOs, incident response, and security-by-default pipeline design
A customer-focused mindset — able to balance developer experience with security and compliance requirements
Demonstrated ability to identify process gaps and drive continuous improvement initiatives
Bonus: experience running LLM/agentic systems, GPUs, or model-serving infrastructure in production
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