Define the end-to-end architecture for AI solutions, including data pipelines, model training environments, and deployment strategies (MLOps).
Evaluate and select the tech stack (e.g., LLMs, Computer Vision frameworks, Vector Databases) that will support the goal of a 2-week testing cycle.
Establish standards for model versioning, monitoring, and "AI Ethics" to ensure that automated validations are reliable, repeatable, and compliant.
Ensure AI solutions are modular and can be integrated across different vehicle platforms and software domains without starting from scratch.
Act as a mentor to AI Developers, conduct code/architecture reviews, and troubleshoot complex system-level bottlenecks.
Collaborate with IT, Security, and Cloud Infrastructure teams to ensure the AI platform is robust and meets Ford’s enterprise standards.

Proven experience in designing microservices architectures and distributed systems.
Deep understanding of Deep Learning, Natural Language Processing (NLP), and Generative AI (LLMs).
Experience with tools like Kubeflow, MLflow, or SageMaker to automate the lifecycle of AI models.
Expert knowledge of Azure or GCP, specifically regarding GPU provisioning, containerization (Docker/Kubernetes), and serverless computing.
Ability to design data lakes and warehouses that provide the "fuel" for AI automation.
Typically 5+ years in software engineering, with at least 3 years in a lead or architectural role focused on AI/ML.

AI HiL Architect – Software Development

Key skills