Software Engineering Manager

As a Software Engineering Manager within MS TECH Order Fulfillment, you will provide strategic product and technical leadership, along with hands-on expertise, to build industry-leading products. You are a systems thinker capable of driving large-scale transformations that maximize value for Ford, our Dealers, and our customers. The role combines high-level strategy, including product vision, AI/ML use-case portfolio, data strategy, and technical roadmaps, with disciplined execution to deliver scalable, resilient, secure, explainable, and highly available solutions in a global environment.

- Experience: 10+ years of progressive software engineering, digital product, data, or AI/ML solution delivery experience, with a significant portion in engineering and product leadership roles.

- AI/ML Product Leadership: Demonstrated experience strategizing, developing, launching, and scaling AI/ML-based products that address business requirements and deliver measurable operational or customer outcomes.

- Product Strategy: Experience defining product vision, business cases, roadmaps, prioritization frameworks, MVPs, go-to-market or launch plans, adoption strategies, and value-realization metrics for data and AI products.

- Education: Undergraduate degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Statistics, Operations Research, or a related quantitative field.

- Certifications: Industry certifications relevant to software engineering, cloud, data, or AI/ML, or a commitment to obtain them within 6 months. GCP Professional Cloud Architect, Professional Machine Learning Engineer, or equivalent certification is a plus.

- Cloud Expertise: 4+ years of experience delivering production solutions on Google Cloud Platform (GCP), including cloud-native application and data/AI services.

- Technical Depth: Expertise in microservices, cloud-native architectures, event-driven architectures, APIs, Domain-Driven Design (DDD), distributed systems, and secure enterprise integration.

- AI/ML & Analytics: Strong working knowledge of supervised and unsupervised learning, time-series forecasting, optimization, anomaly detection, feature engineering, model evaluation, experimentation, and production inference patterns.

- Data Engineering: Experience with data architectures, data pipelines, data quality, governance, metadata and lineage, feature stores, batch and streaming data, and analytics platforms.

- MLOps / LLMOps: Hands-on experience establishing or governing CI/CD/CT for models, experiment tracking, model registry, automated validation, deployment, observability, drift monitoring, retraining, and lifecycle controls.

- Responsible AI: Experience applying secure and responsible AI practices, including privacy, transparency, explainability, bias and fairness assessment, human oversight, access controls, risk management, and auditability.

- Technology Stack: Hands-on experience with Java, Angular, Python, SQL, Terraform, Postgres, APIGEE, Kubernetes, Docker, serverless technologies, and containerization. Experience with Vertex AI, BigQuery, Dataflow, Pub/Sub, or equivalent cloud services is strongly preferred.

- Engineering Excellence: Thorough knowledge of multi-threading, concurrency, parallel processing, DevSecOps, test automation, and monitoring tools such as Dynatrace or Google Cloud Monitoring.

- Developer Experience: Experience increasing developer productivity by integrating AI agents, coding assistants, reusable platform capabilities, or AI skills into the development lifecycle.

- Leadership Qualities: Proven ability to lead large-scale transformations, apply systems thinking, create psychologically safe teams, influence complex decisions, and earn the respect of strong individual technical talent through competence and mentorship.

- Communication & Business Acumen: Ability to communicate complex technical and AI concepts to executives and business partners, align diverse stakeholders, manage trade-offs, and connect product investments to business outcomes.

Nice to Have

- Advanced degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Statistics, Operations Research, or a related field.

- Experience with Vertex AI, BigQuery, Feature Store, Gemini or other foundation-model platforms, vector search, retrieval-augmented generation (RAG), agentic workflows, and evaluation frameworks.

- Experience building reusable enterprise platforms and underlying services for data, analytics, and AI capabilities.

- Experience with forecasting, supply chain, order fulfillment, demand planning, optimization, or decision intelligence products.

- Ability to translate product roadmaps into manageable features through quarterly scoping sessions and assist product teams directly with technical blockers.

- Proven ability to identify and mitigate delivery, data, model, security, adoption, and operational risks while assessing overall product health and prompting timely decisions.

- Strong understanding of business priorities and technical feasibility to prioritize platform backlogs and manage dependencies.

- Experience with Lean methodology, eXtreme Programming (XP), Agile product management, and Human-Centered Design.

- Experience championing modern software, data, AI/ML, product, and responsible AI practices within a large organization.