Principal Software Engineer

We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves and build a better world -- together. At Ford, we’re all a part of something bigger than ourselves. What will you make today?

The Ford Motor Credit Company team helps put people behind the wheels of great Ford and Lincoln vehicles. By partnering with dealerships, we provide financing, personalized service and professional expertise to five thousand dealers and more than four million customers in over one hundred countries around the world.

Ford Credit is actively seeking an experienced, hands-on Principal Architect to spearhead our Cloud Native modernization on Google Cloud Platform (GCP). The ideal candidate will bring a deep understanding of cloud-native technologies, CI/CD, DR / public traffic security and scaling, observability, automation, and best practices. Prior experience in Machine Learning Operations (MLOps) and GraphQL is a strong plus. As a Principal Architect, you will not only design and implement cutting-edge cloud-native architectures but also collaborate with various teams to ensure optimum performance, security, and scalability.  This position will report directly to the VP of Enterprise Architecture & Innovation.

If you are a hands-on Principal Architect with a strong background in GCP and cloud-native technologies, and enjoy working in a collaborative environment with a fantastic team-oriented culture, we encourage you to apply. Experience with MLOps and GraphQL would be a significant advantage. Join our team in driving innovation and transforming our applications and infrastructure.

What you’ll do…

• Lead the design and hands-on implementation of cloud-native architectures and solutions on GCP, using industry best practices and standards.
• Lead engineering teams to implement robust CI/CD processes, automation, and standards to enable efficient, reliable, and frequent deployment of applications and infrastructure.
• Champion observability by creating and implementing monitoring, logging, and tracing systems that provide actionable insights into system and process performance, behavior, and health.
• Drive automation initiatives to streamline processes, increase efficiency, and minimize manual intervention in the deployment, monitoring, and management of cloud-native infrastructure.
• Work collaboratively with cross-functional teams, including engineers, developers, and product owners, to define technical requirements and devise solutions that align with business objectives.
• Continuously evaluate and integrate new GCP features, services, and technologies to enhance the performance, reliability, and scalability of our cloud-native applications and infrastructure.
• Develop and maintain documentation for cloud-native infrastructure, architecture, best practices, and workflows.
• Mentor and guide team members in the adoption of cloud-native technologies, best practices, and methodologies.
• Identify, troubleshoot, and resolve complex technical issues related to cloud-native applications and infrastructure.
• Facilitate the integration of machine learning workflows into our operations, if applicable, ensuring the scalability, reproducibility, and efficiency of ML models.

 

You’ll have….

• Bachelor’s degree in computer science, engineering, or a related field. 
• Minimum of 10 years of experience in software development, infrastructure engineering, or a related field.
• Minimum of 5 years of hands-on experience with GCP, AWS, or Azure, including designing, deploying, and managing cloud-native applications and infrastructure.
• Demonstrated experience with programming and scripting skills in languages such as Python, Go, or Java.
• Demonstrated experience of use of containerization technologies, including Docker and Kubernetes.
• Demonstrated experience in analytical, problem-solving, and communication skills, with the ability to work effectively in a collaborative team environment.

 

Even better, you may have….

• Master's degree in computer science, engineering, or a related field
• Experience with Machine Learning Operations (MLOps) and deploying machine learning models in production environments.
• Experience with GraphQL and creating efficient, flexible APIs.
• GCP Professional Cloud Architect or GCP Professional Machine Learning Engineer certification.
• Experience with other cloud platforms, such as AWS or Azure.
• Familiarity with service mesh technologies.
• In-depth knowledge of GCP services such as Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, Firestore, BigQuery, and Vertex AI.
• Strong experience with infrastructure as code (IaC) tools such as Terraform, and configuration management tools like Ansible or Puppet.