AI HiL Architect - Software Development

4 Days On Site: GTBC Ford México (Naucalpan, México)

Architectural Design: Define the end-to-end architecture for AI solutions, including data pipelines, model training environments, and deployment strategies (MLOps).

Technology Roadmap: 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.

MLOps & Governance: Establish standards for model versioning, monitoring, and "AI Ethics" to ensure that automated validations are reliable, repeatable, and compliant.

Scalability & Integration: Ensure AI solutions are modular and can be integrated across different vehicle platforms and software domains without starting from scratch.

Technical Leadership: Act as a mentor to AI Developers, conduct code/architecture reviews, and troubleshoot complex system-level bottlenecks.

Cross-Functional Alignment: Collaborate with IT, Security, and Cloud Infrastructure teams to ensure the AI platform is robust and meets Ford’s enterprise standards.

System Design: Proven experience in designing microservices architectures and distributed systems.

Advanced AI/ML: Deep understanding of Deep Learning, Natural Language Processing (NLP), and Generative AI (LLMs) architecture.

MLOps Mastery: Experience with tools like Kubeflow, MLflow, or SageMaker to automate the lifecycle of AI models.

Cloud Infrastructure: Expert knowledge of Azure or GCP, specifically regarding GPU provisioning, containerization (Docker/Kubernetes), and serverless computing.

Data Strategy: Ability to design data lakes and warehouses that provide the "fuel" for AI automation.

Experience:Typically 5+ years in software engineering, with at least 3 years in a lead or architectural role focused on AI/ML.