Machine Learning Engineer Data Scientist

- Typically 2 to 3 years of experience with Python and cloud engineering, including Google Cloud services such as Vertex AI. Candidates who have built comparable skills through other paths are encouraged to apply. - Hands-on experience with core technologies such as Python, Google Cloud Platform (Vertex AI, BigQuery, Cloud Storage, GKE, Dataflow, Pub/Sub), Docker, Git, and scripting languages such as Bash or PowerShell. - Familiarity with ML workflow orchestration tools (e.g., Airflow, Kubeflow, Vertex AI Pipelines) and MLOps practices, including CI/CD, infrastructure as code, application security scanning, and batch vs. streaming deployment architectures.

What you'll do... - Partner with the Technical Lead, Product Manager, and Product Owner to design and deliver a scalable MLOps platform on GCP, using Python and modern cloud services, that empowers data scientists across the company. - Deploy machine learning models and algorithms into production, running simulations and testing multiple scenarios to validate performance and reliability.

- A bachelor’s degree in Computer Science, Computer Engineering, or a related technical discipline, or equivalent practical experience. - Typically 2 to 3 years of experience with Python and cloud engineering, including Google Cloud services such as Vertex AI. Candidates who have built comparable skills through other paths are encouraged to apply. - Hands-on experience with core technologies such as Python, Google Cloud Platform (Vertex AI, BigQuery, Cloud Storage, GKE, Dataflow, Pub/Sub), Docker, Git, and scripting languages such as Bash or PowerShell.

Why Join Ford of Mexico? Celebrating over 100 years of legacy in Mexico, we are more than just a business we are a vibrant, collaborative community of over 14,000 innovators shaping the global future of transportation. At Ford, we build what matters, grow where it counts, and drive progress with heart. Whether you are collaborating at our state-of-the-art Global Technology and Business Center (GTBC) or manufacturing iconic, award-winning vehicles in our world-class plants, you will find a culture evolving together, built on trust, passion, and shared purpose. Here, your work moves people forward, and your growth is personal. Join us, and let’s build what’s next together. In this position... Our Enterprise Analytics & AI team builds the internal platforms that let data scientists across Ford turn ideas into production-grade machine learning. This role exists to make that journey faster, more reliable, and more repeatable. You will help create and evolve an MLOps platform on Google Cloud Platform (GCP) that standardizes how models are developed, deployed, trained, and retrained. Working in a cross-functional team with engineers, product managers, and business partners, you will deliver platform and data products that help Ford scale AI, speed up decision-making, and bring intelligent solutions to customers and operations. What you'll do...

- Automate model deployment, training, and retraining through CI/CD and continuous training (CT) pipelines, applying Agile delivery practices and release strategies such as canary and blue/green. - Build and integrate RESTful APIs that connect systems and services across the ML platform, using containerized and Kubernetes-based environments. - Define and standardize machine learning development practices, using Git-based workflows and experimentation to drive continuous innovation.

- A master’s degree in Computer Science, Machine Learning, or a related field. - Experience with Terraform, Tekton, or similar infrastructure-as-code and CI/CD tools; ML frameworks such as scikit-learn, TensorFlow, and PyTorch; software craftsmanship practices such as test-driven development and paired programming; and knowledge of generative AI concepts and technologies. Ford of Mexico is committed to being an equal opportunity employer. We believe that a diverse and inclusive workplace is essential to our success. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, pregnancy, veteran status, or disability status.

- Hands-on experience with core technologies such as Python, Google Cloud Platform (Vertex AI, BigQuery, Cloud Storage, GKE, Dataflow, Pub/Sub), Docker, Git, and scripting languages such as Bash or PowerShell. - Familiarity with ML workflow orchestration tools (e.g., Airflow, Kubeflow, Vertex AI Pipelines) and MLOps practices, including CI/CD, infrastructure as code, application security scanning, and batch vs. streaming deployment architectures. - Strong written and verbal English communication skills, and a collaborative approach to working with engineering, product, design, and business teams.