AI Engineering ManagerFord Motor Companyfull-timeRequired skillsBigQuerycloud infrastructurecompliancecomputer visioncontainerizationcross-functionaldata scienceDataflowdeep learningDockerend-to-endGCPgenetic algorithmsGitGoogle CloudKerasmachine learningNLPpredictive analyticsproduct deliveryregressionSparkSQLstatisticsteam developmentTensorFlowROI analysisPytorchVertexAbout the role Ford Motor Company Website: ford.com Job details: Strategic Thinking & LeadershipPartner with business leaders to identify high-impact AI opportunities and translate them into scalable AI/ML solutions.Define and communicate AI product vision, roadmaps, and measurable success metrics.Drive AI strategy across predictive analytics, Generative AI, and intelligent automation initiatives.Establish governance frameworks for Responsible AI, model explainability, fairness, and compliance.Lead cross-functional AI programs and influence executive stakeholders through compelling insights and presentations.Technical Leadership & ExpertiseArchitect and oversee end-to-end AI/ML and GenAI systems, including:Predictive analytics modelsDeep learning and neural networksNLP and computer vision solutionsRetrieval-Augmented Generation (RAG) systemsAgentic AI frameworks and multi-agent orchestration systemsStrong proficiency in Google Cloud Platform (GCP) services for AI/ML (Vertex AI, BigQuery, Dataflow, Cloud Storage)Deep expertise in machine learning algorithms including ensemble methods, neural networks, regression models, simulation and optimization techniques, NLP, and image processingExperience building AI systems using TensorFlow, PyTorch, Keras, and Python-based ecosystemsExperience with LLMs, foundation models, prompt engineering, fine-tuning, and evaluation pipelinesImplement scalable MLOps and LLMOps practices including CI/CD for ML, model versioning, monitoring, and automated retrainingProficiency in Git, Docker, API-based deployments, and scalable cloud AI servicesApply strong software engineering practices within AI systems including testing, modular design, observability, and documentationDrive research and innovation in advanced AI techniques to enhance enterprise capabilitiesSupport architectural reviews and ensure best practices across AI systemsImplement Responsible AI principles including governance, model explainability, fairness, and ethical AI complianceDelivery FocusOwn end-to-end AI product delivery in partnership with Product, Engineering, and Data teams.Ensure production-grade deployment of AI models using containerization (Docker), orchestration, and scalable cloud infrastructure.Influence investment decisions using measurable impact metrics and ROI analysis.Establish monitoring frameworks for model drift, performance degradation, and system reliability.Team Development & Community LeadershipLead and mentor AI engineers and data scientists.Build AI engineering standards, reusable frameworks, and shared tooling across SSDA.Promote knowledge sharing through Communities of Practice.Foster a culture of experimentation, continuous learning, and engineering excellence.Support talent development in emerging AI domains including GenAI and agent-based systems.Strategic Thinking & LeadershipPartner with business leaders to identify high-impact AI opportunities and translate them into scalable AI/ML solutions.Define and communicate AI product vision, roadmaps, and measurable success metrics.Drive AI strategy across predictive analytics, Generative AI, and intelligent automation initiatives.Establish governance frameworks for Responsible AI, model explainability, fairness, and compliance.Lead cross-functional AI programs and influence executive stakeholders through compelling insights and presentations.Technical Leadership & ExpertiseArchitect and oversee end-to-end AI/ML and GenAI systems, including:Predictive analytics modelsDeep learning and neural networksNLP and computer vision solutionsRetrieval-Augmented Generation (RAG) systemsAgentic AI frameworks and multi-agent orchestration systemsStrong proficiency in Google Cloud Platform (GCP) services for AI/ML (Vertex AI, BigQuery, Dataflow, Cloud Storage)Deep expertise in machine learning algorithms including ensemble methods, neural networks, regression models, simulation and optimization techniques, NLP, and image processingExperience building AI systems using TensorFlow, PyTorch, Keras, and Python-based ecosystemsExperience with LLMs, foundation models, prompt engineering, fine-tuning, and evaluation pipelinesImplement scalable MLOps and LLMOps practices including CI/CD for ML, model versioning, monitoring, and automated retrainingProficiency in Git, Docker, API-based deployments, and scalable cloud AI servicesApply strong software engineering practices within AI systems including testing, modular design, observability, and documentationDrive research and innovation in advanced AI techniques to enhance enterprise capabilitiesSupport architectural reviews and ensure best practices across AI systemsImplement Responsible AI principles including governance, model explainability, fairness, and ethical AI complianceDelivery FocusOwn end-to-end AI product delivery in partnership with Product, Engineering, and Data teams.Ensure production-grade deployment of AI models using containerization (Docker), orchestration, and scalable cloud infrastructure.Influence investment decisions using measurable impact metrics and ROI analysis.Establish monitoring frameworks for model drift, performance degradation, and system reliability.Team Development & Community LeadershipLead and mentor AI engineers and data scientists.Build AI engineering standards, reusable frameworks, and shared tooling across SSDA.Promote knowledge sharing through Communities of Practice.Foster a culture of experimentation, continuous learning, and engineering excellence.Support talent development in emerging AI domains including GenAI and agent-based systems.Minimum RequirementsBachelor’s Degree in a related field (Data Science, Machine Learning, Computer Science, Statistics, Applied Mathematics, IT, or equivalent).5 to 8 years of experience applying analytical methods and AI/ML solutions in enterprise environments.5 to 8 years of experience using Python-based AI/ML technologies.Experience leading AI or Data Science teams.Experience acting as a senior technical lead facilitating solution trade-offs and architectural decisions.Experience using Cloud AI Platforms (GCP preferred).Hands-on experience with Generative AI technologies and enterprise AI deployment.Preferred RequirementsMaster’s or PhD in Data Science, Machine Learning, Statistics, Applied Mathematics, or Computer Science.Experience managing and growing high-performing AI teams.Expert-level knowledge in advanced predictive analytics and AI techniques (Genetic Algorithms, Ensemble Learning, Neural Networks, NLP, Simulation, Design of Experiments).Strong working knowledge of GCP and enterprise AI architecture patterns.Expertise in open-source technologies such as Python, R, Spark, SQL.Experience building enterprise-grade GenAI and agent-based AI solutions. 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