Software Engineering Manager

Description 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. Responsibilities Technical Leadership & Vision

• Serve as the primary technical authority for the Order Generation product suite, defining the evolution of the technology stack, data architecture, AI/ML capabilities, and architectural patterns.
• Lead cross-functional teams through complex integrations, managing dependencies across the broader Order Fulfillment ecosystem to ensure seamless data flow and system interoperability.
• Translate high-level business requirements into actionable technical strategies that align with Ford enterprise standards. AI/ML Product Strategy & Innovation
• Define and execute an AI/ML and data analytics product strategy that converts priority business requirements into a sequenced portfolio of intelligent capabilities and measurable outcomes.
• Identify, evaluate, and prioritize AI/ML opportunities across forecasting, order generation, decision support, anomaly detection, optimization, and workflow automation using value, feasibility, risk, data readiness, and adoption criteria.
• Lead the end-to-end lifecycle of AI/ML products from discovery, business-case development, experimentation, and MVP validation through industrialization, launch, adoption, and continuous improvement.
• Partner with Data Science, Data Engineering, Product, Architecture, Cybersecurity, Legal, Privacy, and business teams to ensure solutions are technically sound, usable, compliant, and aligned with responsible AI principles.
• Establish outcome-based product metrics, experimentation methods, model performance targets, and adoption measures; use evidence and customer feedback to guide investment and roadmap decisions.
• Monitor emerging technologies, including generative AI, agentic AI, foundation models, advanced analytics, optimization, and intelligent automation, and determine where they can create differentiated business value.
• Drive build, buy, or partner assessments and develop scalable patterns for reusable AI/ML services, data products, model APIs, and decision intelligence capabilities. Product Strategy & Delivery
• Partner with business stakeholders to define and execute a multi-year product vision and roadmap focused on optimized order forecasting and generation.
• Champion an iterative, Agile delivery model, prioritizing the delivery of Minimum Viable Products (MVPs) and maintaining a high-velocity release cadence.
• Apply Human-Centered Design (HCD) principles to ensure technical solutions solve real-world problems for Dealers and customers.
• Create launch and adoption plans that include operational readiness, user training, change management, benefit tracking, and feedback loops. Data, Model & MLOps Excellence
• Ensure AI/ML solutions are supported by trusted, governed, discoverable, and fit-for-purpose data, with clear ownership, lineage, quality controls, and access patterns.
• Guide the implementation of robust MLOps and LLMOps practices covering reproducible experimentation, model registry, automated testing, deployment, monitoring, drift detection, retraining, rollback, and auditability.
• Define controls for model quality, explainability, bias and fairness evaluation, privacy, security, human oversight, and responsible use throughout the product lifecycle.
• Balance predictive accuracy with interpretability, latency, cost, reliability, and business usability when selecting models and architectures. Engineering & Operational Excellence
• Enforce rigorous engineering standards, including Test-Driven Development (TDD), robust CI/CD pipelines, and DevSecOps practices.
• Drive a culture of Full Lifecycle Ownership, where the team is responsible for the design, security, deployment, and operational health of its services.
• Establish and monitor key performance indicators (KPIs) for system health, code quality, delivery velocity, model performance, data quality, adoption, and realized business value. Architectural Design
• Architect and oversee the development of cloud-native, microservices-based systems designed for global scale, multi-tenancy, and high-performance transactional processing.
• Design interoperable data and AI architectures that support batch and real-time inference, event-driven workflows, APIs, observability, and secure integration with enterprise platforms. People Leadership & Talent Development
• Cultivate a high-performing, diverse team of Software Engineers, Product Managers, Data Engineers, Data Scientists, and ML Engineers through active coaching, mentorship, and career pathing.
• Foster a culture of psychological safety and continuous learning, utilizing blameless retrospectives and regular feedback loops to drive team growth.
• Identify and close skill gaps within the team to keep pace with emerging technologies and industry trends. Strategic Collaboration
• Act as a bridge between the product team and domain experts in Cloud Infrastructure, Data & AI, Cybersecurity, Responsible AI, SRE, and DevOps to reduce portfolio complexity.
• Influence stakeholders across the organization to adopt modern engineering practices, responsible AI controls, reusable data products, and standardized service contracts. Technical Execution (Hands-on)
• Maintain deep technical fluency in the team’s primary languages, frameworks, cloud services, data platforms, and AI/ML toolchain, including Java, Spring Boot, GCP/Azure, Vertex AI, and modern MLOps platforms.
• Lead from the front by participating in architecture and code reviews, resolving complex technical blockers, reviewing model and data design decisions, and occasionally prototyping high-risk or emerging technology concepts. Qualifications
• 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 p