Java Full Stack Engineer
Key Skills for This Role
Full Job Posting
Qualifications
- Experience building enterprise platforms for document ingestion, search, workflow automation, data products, or AI-assisted user experiences.
- Knowledge of Python and common AI/ML frameworks in addition to the primary Java and Angular stack.
- Google Cloud certifications or demonstrated equivalent expertise in cloud architecture, application development, DevOps, data engineering, or machine learning.
- Experience in automotive, manufacturing, or another large-scale regulated enterprise environment.
- 8+ years of progressive software or platform engineering experience, including substantial hands-on delivery of enterprise full-stack applications and cloud-native platforms.
- Expert-level Java and Spring Boot development, including microservices, REST APIs, security, testing, performance tuning, and production troubleshooting.
- Strong Angular and TypeScript expertise, including component architecture, state management, reusable UI patterns, responsive design, accessibility, and automated testing.
- Solid understanding of relational databases, SQL, API integration, authentication and authorization, and distributed-system design.
- Exceptional hands-on proficiency across GCP compute, serverless, container, messaging, workflow, storage, database, networking, IAM, observability, security, and cost-management services.
- Proven ability to select appropriate GCP services, design production architectures, automate provisioning, and diagnose complex cloud issues.
- Strong practical knowledge of current AI tools and services, including Vertex AI, Gemini or comparable foundation models, AI coding assistants, embeddings, vector databases, RAG, prompt engineering, evaluation, guardrails, and agentic workflows.
- Experience integrating AI services into enterprise applications with attention to data privacy, security, responsible AI, reliability, latency, and cost.
- Strong experience with Git, Docker, Kubernetes, CI/CD, automated testing, code-quality tooling, observability, and Infrastructure as Code using Terraform.
- Excellent analytical, communication, collaboration, and technical leadership skills, with the ability to work effectively across product, UX, platform, data, security, and AI/ML teams.
Responsibilities
- As a Platform Engineer, you will balance hands-on architecture and coding with technical mentorship, driving engineering standards, MLOps best practices, Platform reliability, and cross-team alignment.
- Key Responsibilities
- Full-Stack Platform Engineering
- Design and develop responsive, accessible, and maintainable platform user interfaces using Angular, TypeScript, HTML, and CSS.
- Build secure, scalable microservices and REST APIs using Java and Spring Boot, with clear contracts between frontend and backend components.
- Own features end to end, including solution design, coding, automated testing, deployment, production monitoring, troubleshooting, and continuous improvement.
- Conduct design and code reviews, establish reusable engineering patterns, and mentor engineers on full-stack and cloud-native development practices.
- GCP Platform Architecture & Engineering
- Design cloud-native solutions on GCP using services such as Cloud Run, Google Kubernetes Engine, Cloud Functions, Pub/Sub, Cloud Tasks, Workflows, Cloud Scheduler, API management, and load balancing.
- Engineer data and application integrations using Cloud Storage, Cloud SQL, BigQuery, Secret Manager, IAM, networking, logging, monitoring, and security controls.
- Build resilient, observable, and cost-efficient services with appropriate scaling, fault tolerance, retry, disaster recovery, and performance patterns.
- AI-Enabled Platform Capabilities
- Integrate current AI and generative AI capabilities into platform workflows using Vertex AI, Gemini models, model APIs, embeddings, vector search, retrieval-augmented generation (RAG), and agent-based patterns where appropriate.
- Use modern AI-assisted engineering tools effectively for design, coding, testing, documentation, and troubleshooting while applying secure-development and human-review controls.
- Implement prompt management, evaluation, guardrails, observability, responsible AI practices, and output validation for production AI features.
- Platform Reliability
- Drive platform availability, performance, security, accessibility, operational readiness, cost optimization, and production support across frontend, backend, cloud, and AI components.
- Develop automated unit, integration, API, UI, security, and performance tests and maintain CI/CD pipelines and infrastructure as code.
- Experience: 8+ years of progressive software or platform engineering experience, including substantial hands-on delivery of enterprise full-stack applications and cloud-native platforms.
- Full-Stack Engineering:
- Expert-level Java and Spring Boot development, including microservices, REST APIs, security, testing, performance tuning, and production troubleshooting.
- Strong Angular and TypeScript expertise, including component architecture, state management, reusable UI patterns, responsive design, accessibility, and automated testing.
- Solid understanding of relational databases, SQL, API integration, authentication and authorization, and distributed-system design.
- Google Cloud Platform:
- Exceptional hands-on proficiency across GCP compute, serverless, container, messaging, workflow, storage, database, networking, IAM, observability, security, and cost-management services.
- Proven ability to select appropriate GCP services, design production architectures, automate provisioning, and diagnose complex cloud issues.
- AI and GenAI Engineering:
- Strong practical knowledge of current AI tools and services, including Vertex AI, Gemini or comparable foundation models, AI coding assistants, embeddings, vector databases, RAG, prompt engineering, evaluation, guardrails, and agentic workflows.
- Experience integrating AI services into enterprise applications with attention to data privacy, security, responsible AI, reliability, latency, and cost.
- DevOps & Engineering Excellence:
- Strong experience with Git, Docker, Kubernetes, CI/CD, automated testing, code-quality tooling, observability, and Infrastructure as Code using Terraform.
- Excellent analytical, communication, collaboration, and technical leadership skills, with the ability to work effectively across product, UX, platform, data, security, and AI/ML teams.
About Ford Global Career Site
Global automotive manufacturer of cars, trucks, and mobility solutions.
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