Senior AI Software Team Manager

The AI Software Team Manager is responsible for leading the strategy, development, and deployment of enterprise-scale AI software solutions that improve manufacturing performance and business results. 
This role provides technical leadership for multidisciplinary teams, ensures execution under corporate standards, collaborates with global leadership organizations, and drives innovation through AI, advanced robotics, intelligent automation, and digital transformation initiatives. 

The position plays a critical role in accelerating AI adoption, developing workforce capabilities, and delivering scalable solutions that enhance frontline operations, operational excellence, and key business metrics across IEPC, CSAP, and Global FPS organizations.

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.
• Application of software design patterns and enterprise architecture principles.

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, including fine-tuning, prompt engineering, and specialized models.
• Design of synchronous and asynchronous inference pipelines.
• Implementation of model routing, fallback mechanisms, caching, batching, and latency/cost optimization strategies.

LLMs & Generative AI

• Strong understanding of transformers, embeddings, tokenization, attention mechanisms, and context management.
• Advanced prompt engineering and structured output generation.
• RAG architectures including chunking, embeddings, vector search, hybrid search, and reranking.
• Experience with fine-tuning, LoRA/PEFT methodologies, and model evaluation.
• Function and tool calling integration.
• Agentic AI, planning, memory management, workflows, and multi-agent systems.
• Model Context Protocol (MCP) and tool orchestration architectures.
• Design and implementation of guardrails, content filtering, and model behavior controls.

Machine Learning

• Solid foundation in supervised learning, unsupervised learning, and deep learning.
• Experience with classification, regression, clustering, and recommendation systems.
• Knowledge of neural networks, CNNs, RNNs, and Transformer architectures.
• Model evaluation, overfitting prevention, data leakage mitigation, and performance metrics.
• Feature engineering and experimentation methodologies.
• Practical expertise with frameworks such as PyTorch, TensorFlow, or equivalent technologies.

Data Engineering

• Design and management of data lakes, data warehouses, and lakehouse architectures.
• Development of ETL/ELT processes and scalable data pipelines.
• Streaming platforms and distributed data processing.
• Data quality, lineage, cataloging, and governance practices.
• Expertise in SQL and NoSQL databases.
• Experience with vector databases and search engines.
• Design of datasets for AI model training, validation, and evaluation.

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.
• Infrastructure as Code (IaC) using Terraform or equivalent technologies.
• Networking fundamentals, load balancing, service discovery, and auto-scaling.
• Multi-region architectures and disaster recovery strategies.
• GPU infrastructure management and accelerator utilization optimization.

MLOps / LLMOps

• CI/CD pipelines and automation for AI/ML workflows.
• Model registry management and model/dataset version control.
• Experiment tracking and reproducibility.
• Model deployment, serving, and lifecycle management.
• Monitoring and observability of production AI systems.
• Detection and mitigation of model drift, data drift, and performance degradation.
• Continuous evaluation and validation of LLM-based applications.
• Observability of prompts, token usage, latency, cost, and response quality.

Software Engineering

• Proficiency in programming languages such as Python, Java, Go, TypeScript/JavaScript, C#, or equivalent.
• Strong commitment to Clean Code principles, automated testing, and code reviews.
• Experience with unit, integration, contract, end-to-end, and load testing.
• Expertise in concurrency, asynchronous programming, and distributed computing.
• Git workflows and branching strategies.
• Refactoring and modernization of legacy systems.

Security

• Implementation of OAuth2, OpenID Connect (OIDC), JWT, RBAC, and ABAC.
• Secrets and credential management.
• Encryption of data at rest and in transit.
• Secure API design and development practices.
• Threat modeling and risk analysis.
• Application security aligned with OWASP standards.
• LLM-specific security, including prompt injection, data exfiltration, tool abuse, jailbreaks, and RAG attack mitigation.
• Secure isolation and governance of autonomous agents.

AI Governance & Responsible AI

• Data privacy and protection principles.
• Explainability, traceability, and transparency of AI systems.
• AI risk assessment and mitigation frameworks.
• Human-in-the-loop system design.
• AI auditing and observability.
• Model access governance and usage policies.
• Knowledge of relevant regulatory and compliance frameworks across industries and regions.

Observability & Operations

• Structured logging and monitoring practices.
• Metrics, tracing, and distributed tracing.
• OpenTelemetry implementation and integration.
• Definition and management of SLOs, SLAs, and SLIs.
• Incident response and operational excellence.
• Capacity planning and resource optimization.
• Performance engineering and system optimization.
• Cost monitoring and FinOps practices for AI workloads.

System Design

• Ability to design complex systems from the ground up.
• Evaluation of trade-offs between consistency, availability, latency, and cost.
• Design for large-scale applications serving millions of users and high-volume data environments.
• Fault-tolerant architecture design.
• Backpressure management strategies.
• Design and implementation of queuing and messaging systems.
• Event sourcing and CQRS where appropriate.
• Multi-tenant platform architecture.

Product Architecture

• Translate business requirements into scalable technical capabilities.
• Determine when AI should be utilized and when alternative solutions are more appropriate.
• Define MVP scope and long-term architectural evolution.
• Design reusable APIs, services, and platforms.
• Balance time-to-market, quality, security, and technical debt considerations.
• Establish technical and business success metrics.

Technical Leadership

• Provide technical leadership, coaching, and mentoring.
• Drive and defend architectural decisions.
• Create and maintain Architecture Decision Records (ADRs).
• Conduct architecture, code, and design reviews.
• Collaborate effectively across engineering, product, security, data, and executive teams.
• Communicate complex technical concepts clearly to both technical and non-technical audiences.
• Lead technical organizations and initiatives without necessarily serving as a people manager.

Architectural Thinking

• Ability to identify and evaluate trade-offs rather than pursuing absolute solutions.
• Design systems with long-term evolution and adaptability in mind.
• Distinguish between reversible and irreversible decisions.
• Identify and mitigate single points of failure.
• Anticipate scalability, security, operational, and reliability challenges.
• Evaluate technologies based on measurable business and technical outcomes rather than industry trends.
  
  DISCLAIMER: Ford Motor Company is an Equal Opportunity Employer, as we are committed with a diverse workforce, and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran and basis of disability.
  
   

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• Lead the BCC Innovation & AI Software organization through disciplined governance, standard work, portfolio management, and performance accountability, ensuring alignment of innovation initiatives with business objectives and leading/lagging KPIs.
• Define and execute the Smart Factory and AI strategy, transforming manufacturing challenges into scalable software, AI/ML, automation, robotics, intelligent vision, and digital solutions that improve safety, quality, cost, delivery, productivity, and frontline performance.
• Provide technical and organizational leadership for cross-functional engineering teams, overseeing solution design, architecture, software development, testing, deployment, adoption, support, and continuous improvement under corporate engineering standards.
• 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 that enable long-term scalability and sustainability.
• 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, support stable processes, and deliver measurable operational value.
• Lead strategic initiatives in advanced robotics, autonomous systems, intelligent vision, AI agents, predictive analytics, and digital manufacturing technologies, driving enterprise-wide scalability and operational transformation.
• Govern technical quality, architecture reviews, code reviews, release readiness, cybersecurity compliance, testing strategies, documentation, configuration management, and risk mitigation to ensure secure, reliable, and maintainable solutions.
• Drive data-informed decision making by ensuring the integrity, quality, traceability, accessibility, and governance of manufacturing, operational, and business data across connected platforms and systems.
• Develop and execute AI capability-building and workforce transformation strategies, establishing role-based learning roadmaps, mentoring AI Software Engineers, and accelerating AI adoption across IEPC, CSAP, and global manufacturing organizations.
• Represent the organization in executive forums, LATAM Innovation Reviews, leadership report-outs, and Global FPS engagements, effectively translating technical initiatives into business impact, strategic value, and investment opportunities.
• 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 into core business processes.
• Foster a culture of innovation, technical excellence, ownership, continuous learning, and engineering craftsmanship while growing high-performing teams capable of delivering next-generation manufacturing technologies.