AI-Accelerated Full Stack Software Development Engineer

We're seeking an experienced Full Stack Software Development Engineer to join our Vehicle Software and Systems Engineering (VSSE) team. As a key member of our team, you will design, build, and maintain secure and scalable web applications using Angular and cloud-native services on Google Cloud Platform (GCP). You will apply AI-assisted development practices to improve productivity, code quality, test coverage, documentation, and delivery speed while helping transform early-stage AI prototypes into secure, production-ready enterprise applications.

Responsibilities:

• Design, develop, test, and maintain full stack web applications using Angular, modern backend services, APIs, and cloud-native technologies on GCP.
• Build responsive, intuitive, and performant user interfaces that simplify complex engineering workflows.
• Develop backend services, RESTful APIs, data integrations, and reusable components to support AI-enabled applications.
• Integrate applications with enterprise data sources, authentication systems, engineering tools, and AI services.
• Ensure applications are secure, scalable, reliable, observable, and maintainable in production environments.
• Participate in architecture reviews, technical design discussions, code reviews, and production readiness assessments.

• Use AI coding assistants and generative AI tools to accelerate software development, refactoring, debugging, documentation, and code reviews.
• Apply AI to support requirements analysis, user story refinement, acceptance criteria generation, design exploration, and technical documentation.
• Leverage AI to generate and improve unit tests, integration tests, end-to-end tests, regression tests, and test data.
• Use AI-assisted approaches for defect triage, log analysis, root-cause investigation, and production support.
• Identify opportunities to automate repetitive SDLC activities and improve developer productivity.
• Promote responsible and secure use of AI tools while protecting Ford data, intellectual property, and enterprise standards.

• Build, deploy, and support applications using Google Cloud Platform services and cloud-native architecture patterns.
• Work with GCP services such as Cloud Run, Cloud Functions, App Engine, GKE, Cloud Storage, Pub/Sub, Firestore, BigQuery, Cloud SQL, Secret Manager, Cloud Build, Artifact Registry, and Cloud Monitoring, as applicable.
• Support CI/CD pipelines, automated testing, containerization, infrastructure automation, and environment management.
• Collaborate with DevSecOps and platform teams to improve deployment reliability, scalability, performance, and observability.
• Apply cloud security best practices, including identity and access management, secrets management, network controls, and data protection.

• Help build and enhance AI-enabled applications that support Ford Product Development teams across electrical, software, and vehicle systems domains.
• Integrate applications with LLMs, AI APIs, retrieval systems, vector databases, enterprise knowledge sources, and workflow automation tools.
• Contribute to reusable AI platform capabilities such as prompt templates, AI service wrappers, evaluation workflows, telemetry, and feedback loops.
• Support low-code or self-service AI capabilities that allow technical and non-technical users to create guided workflows or AI-assisted solutions.
• Explore and apply emerging AI development patterns, including retrieval-augmented generation, agentic workflows, Model Context Protocol, and Agent-to-Agent integration where appropriate.

• Work closely with product owners, designers, data scientists, software engineers, DevOps engineers, and business stakeholders.
• Translate user needs and product requirements into high-quality technical solutions.
• Contribute to backlog refinement, sprint planning, estimation, demos, retrospectives, and delivery planning.
• Use metrics, telemetry, and user feedback to improve application performance, usability, adoption, and business impact.
• Share knowledge with the team on AI-assisted engineering practices, reusable development patterns, and full stack best practices.

Qualifications:

• Bachelor’s Degree in Computer Science, Software Engineering, Engineering, Information Systems, or a related technical field.
• 7+ years of professional software development experience.
• 7+ years of experience building modern web applications using Angular, TypeScript, HTML, CSS/SCSS, and modern frontend engineering practices.
• Experience developing backend services, APIs, and integrations using one or more languages or frameworks such as Node.js, Java, Python, Spring Boot, NestJS, Express, or similar.
• Hands-on experience with Google Cloud Platform or equivalent cloud platform.
• Practical experience using AI coding assistants, generative AI tools, or LLM-based development workflows to accelerate software delivery.
• Understanding of how AI can support the SDLC, including requirements analysis, coding, testing, documentation, debugging, deployment, and operational support.
• Experience with REST APIs, authentication/authorization patterns, secure coding practices, and enterprise application integration.
• Experience with Git, CI/CD pipelines, automated testing, code reviews, and Agile software development practices.
• Ability to write clean, maintainable, well-tested code and troubleshoot complex full stack issues.
• Strong communication and collaboration skills with the ability to work across technical and non-technical teams.