Description
The Senior AI Software Team Manager leads the strategy, development, and deployment of enterprise-scale AI software solutions to improve manufacturing performance and business results at Ford's Irapuato plant.
Responsibilities
- Lead the BCC Innovation & AI Software organization through disciplined governance, standard work, portfolio management, and performance accountability.
- Define and execute the Smart Factory and AI strategy, transforming manufacturing challenges into scalable software, AI/ML, automation, robotics, intelligent vision, and digital solutions.
- Provide technical and organizational leadership for cross-functional engineering teams, overseeing solution design, architecture, software development, testing, deployment, adoption, support, and continuous improvement.
- Architect and govern the AI and Smart Factory ecosystem, establishing standards, reusable platforms, APIs, data models, cloud services, observability frameworks, cybersecurity controls, and enterprise integration patterns.
- Collaborate closely with Plant Leadership, Global FPS, ATP, IT, MARVELL, Engineering, Quality, Maintenance, Production, and other business functions to ensure technology solutions address real business problems.
- Lead strategic initiatives in advanced robotics, autonomous systems, intelligent vision, AI agents, predictive analytics, and digital manufacturing technologies.
- Govern technical quality, architecture reviews, code reviews, release readiness, cybersecurity compliance, testing strategies, documentation, configuration management, and risk mitigation.
- Drive data-informed decision making by ensuring the integrity, quality, traceability, accessibility, and governance of manufacturing, operational, and business data.
- Develop and execute AI capability-building and workforce transformation strategies, establishing role-based learning roadmaps, mentoring AI Software Engineers, and accelerating AI adoption.
- Represent the organization in executive forums, LATAM Innovation Reviews, leadership report-outs, and Global FPS engagements.
- Lead application lifecycle management, including deployment readiness, adoption monitoring, operational support, incident management, technical debt reduction, modernization planning, knowledge transfer, and continuous product evolution.
- Advance the evolution of Daily Management Systems, Operational Excellence, Lean/FPS practices, and Digital Manufacturing by integrating AI-enabled insights, automation, and scalable software solutions.
- Foster a culture of innovation, technical excellence, ownership, continuous learning, and engineering craftsmanship.
Qualifications
Software Architecture
- Design of distributed, scalable, resilient, and maintainable systems.
- Expertise in modular monolithic, microservices, event-driven, and serverless architectures.
- Deep understanding of Domain-Driven Design (DDD), Clean Architecture, Hexagonal Architecture, and SOLID principles.
- Design and implementation of REST APIs, GraphQL, gRPC, and service contracts.
AI Systems Architecture
- Design of end-to-end AI/ML systems, from data acquisition through production inference.
- Architecture of LLM-based, Retrieval-Augmented Generation (RAG), agentic, and multimodal systems.
- Selection and evaluation of proprietary and open-source models.
LLMs & Generative AI
- Strong understanding of transformers, embeddings, tokenization, attention mechanisms, and context management.
- Advanced prompt engineering and structured output generation.
Machine Learning
- Solid foundation in supervised learning, unsupervised learning, and deep learning.
- Experience with classification, regression, clustering, and recommendation systems.
Data Engineering
- Design and management of data lakes, data warehouses, and lakehouse architectures.
- Development of ETL/ELT processes and scalable data pipelines.
Cloud & Infrastructure
- Deep expertise in at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP).
- Kubernetes orchestration, containerization, and Docker ecosystems.
MLOps / LLMOps
- CI/CD pipelines and automation for AI/ML workflows.
- Model registry management and model/dataset version control.
Software Engineering
- Proficiency in programming languages such as Python, Java, Go, TypeScript/JavaScript, C#.
- Strong commitment to Clean Code principles, automated testing, and code reviews.
Security
- Implementation of OAuth2, OpenID Connect (OIDC), JWT, RBAC, and ABAC.
- Secrets and credential management.
AI Governance & Responsible AI
- Data privacy and protection principles.
- Explainability, traceability, and transparency of AI systems.
Observability & Operations
- Structured logging and monitoring practices.
- Metrics, tracing, and distributed tracing.
System Design
- Ability to design complex systems from the ground up.
- Evaluation of trade-offs between consistency, availability, latency, and cost.
Product Architecture
- Translate business requirements into scalable technical capabilities.
- Determine when AI should be utilized and when alternative solutions are more appropriate.
Technical Leadership
- Provide technical leadership, coaching, and mentoring.
- Drive and defend architectural decisions.
Architectural Thinking
- Ability to identify and evaluate trade-offs rather than pursuing absolute solutions.
- Design systems with long-term evolution and adaptability in mind.