Data Scientist MLOPs

Ford Global Data Insight & Analytics is seeking a Machine Learning DevSecOps Engineer to build and advance its internal Data Science / AI/ML platform. You will deliver MLOps capabilities on Google Cloud Platform (GCP), standardize machine learning development practices, and support the deployment, training, and retraining of production ML models.

You will work in a cross-functional team and collaborate directly and continuously with engineers, business partners and product managers, delivering platform/data products.

• Work closely with the Tech Anchor, Product Manager, and Product Owner to deliver an MLOps platform on GCP using Python and other tools for data scientists across the company.

• Innovate and standardize machine learning development practices.

• Experiment, innovate, and share knowledge with the team.

• Use Git for version control and collaborative development.

• Lead by example through paired programming for cross-training, upskilling, problem-solving, and faster delivery.

• Apply ML, NLP, GCP, MLOps, and Kubernetes technologies to platform development.

• Deploy ML models and algorithms into production, run simulations for algorithm development, and test different scenarios.

• Develop and integrate RESTful APIs to connect systems and services within the ML platform.

• Automate model deployment, training, and retraining using Agile delivery practices and CI/CD/CT (continuous integration, continuous deployment, and continuous training).

• Collaborate with software engineers to understand the platform vision, break work into tasks, and resolve complex issues.

• Grow technical capabilities and provide guidance to other software engineers on the team.

• Partner with software and Data Scientist to tackle challenging AI problems.

Experience: 3+ years with Python and cloud engineering / services, including GCP Vertex AI and related services.

Education: Bachelor’s degree in Computer Science, Computer Engineering, or a similar technical discipline. A master’s degree in Computer Science, Machine Learning, or a related field is preferred.

Technical Requirements:

We are looking for candidates with strong Python and cloud engineering skills, experience in ML workflow orchestration and MLOPs, and the ability to support production ML platforms.

Core Technical Requirements:

Python and Cloud Engineering:

• 2+ years of experience with Python and cloud engineering / services such as GCP Vertex AI and related services.

ML Workflow Orchestration:

• Experience with ML workflow orchestration tools such as Airflow or Kubeflow.

MLOPs:

• Experience with MLOPs tools such as Tekton, Terraform, Security tools (SonarQube, FOSSA, Checkmarx), Containarization (Docker).
• Deploying architectures knowledge (batch vs streaming), deploying protocols (Canary, Blue/Green).

Vertex AI

• Model training
• Model deployment
• Model monitoring
• Vertex AI Pipelines

BigQuery

• Data storage and analytics
• Feature engineering
• Training data preparation

Cloud Storage (GCS)

• Dataset storage
• Model artifacts
• Pipeline outputs

GKE (Google Kubernetes Engine)

• Running containerized ML workloads
• Kubeflow deployments
• Scalable serving environments

Kubeflow / Vertex AI Pipelines

• ML workflow orchestration
• Training and deployment automation

Dataflow

• ETL and large-scale data processing

Pub/Sub

• Event-driven and streaming ML architectures

Scripting:

• Experience with scripting languages such as Bash or PowerShell.

Git and Collaborative Development:

• Proficiency in Git for version control and collaboration.

Helpful Additional Experience:

• A master’s degree in Computer Science, Machine Learning, or a related field.
• Infrastructure as code using Terraform.
• Software craftsmanship practices such as paired programming and test-driven development.
• Understanding of MLOps, the machine learning lifecycle, and frameworks such as scikit-learn, TensorFlow, and PyTorch.
• Applying Agile practices to solution delivery.
• Working with Dockerfiles, container images, GitHub, and CI/CD concepts.
• Knowledge of generative AI technologies and concepts.

Collaboration:

Written and verbal English proficiency is required. Candidates should be comfortable collaborating across engineering, product, design, and business teams, sharing knowledge, and supporting the technical growth of colleagues through paired programming and collaborative problem-solving.