{"id":2609560,"url":"https://alion.io/job/ford-motor-company-software-engineering-manager-3","title":"Software Engineering Manager","company":{"id":4094,"name":"Ford Motor Company","domain":"ford.com","url":"https://alion.io/company/ford-motor-company","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":77,"ghost_share":0,"stale_share":0.169,"repost_share":0.013,"time_to_fill_p50_days":3,"computed_at":"2026-10-20T05:45:00Z"}},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":30000,"max_usd":78000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":33},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"GCP","optional":false},{"name":"Interpretability","optional":false},{"name":"Java","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Spring Boot","optional":false},{"name":"Vertex AI","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"Angular","optional":true},{"name":"BigQuery","optional":true},{"name":"Docker","optional":true},{"name":"Dynatrace","optional":true},{"name":"Feature Store","optional":true},{"name":"Gemini","optional":true},{"name":"Google BigQuery","optional":true},{"name":"JavaScript","optional":true},{"name":"Kubernetes","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Python","optional":true},{"name":"RAG","optional":true},{"name":"SQL","optional":true},{"name":"Terraform","optional":true},{"name":"Time Series Forecasting","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-15T07:16:42Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-20T21:26:12Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"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.\n Technical Leadership & Vision\nServe 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.\n\nLead cross-functional teams through complex integrations, managing dependencies across the broader Order Fulfillment ecosystem to ensure seamless data flow and system interoperability.\n\nTranslate high-level business requirements into actionable technical strategies that align with Ford enterprise standards.\n\nAI/ML Product Strategy & Innovation\nDefine 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.\n\nIdentify, 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.\n\nLead 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.\n\nPartner 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.\n\nEstablish outcome-based product metrics, experimentation methods, model performance targets, and adoption measures; use evidence and customer feedback to guide investment and roadmap decisions.\n\nMonitor emerging technologies, including generative AI, agentic AI, foundation models, advanced analytics, optimization, and intelligent automation, and determine where they can create differentiated business value.\n\nDrive build, buy, or partner assessments and develop scalable patterns for reusable AI/ML services, data products, model APIs, and decision intelligence capabilities.\n\nProduct Strategy & Delivery\nPartner with business stakeholders to define and execute a multi-year product vision and roadmap focused on optimized order forecasting and generation.\n\nChampion an iterative, Agile delivery model, prioritizing the delivery of Minimum Viable Products (MVPs) and maintaining a high-velocity release cadence.\n\nApply Human-Centered Design (HCD) principles to ensure technical solutions solve real-world problems for Dealers and customers.\n\nCreate launch and adoption plans that include operational readiness, user training, change management, benefit tracking, and feedback loops.\n\nData, Model & MLOps Excellence\nEnsure AI/ML solutions are supported by trusted, governed, discoverable, and fit-for-purpose data, with clear ownership, lineage, quality controls, and access patterns.\n\nGuide the implementation of robust MLOps and LLMOps practices covering reproducible experimentation, model registry, automated testing, deployment, monitoring, drift detection, retraining, rollback, and auditability.\n\nDefine controls for model quality, explainability, bias and fairness evaluation, privacy, security, human oversight, and responsible use throughout the product lifecycle.\n\nBalance predictive accuracy with interpretability, latency, cost, reliability, and business usability when selecting models and architectures.\n\nEngineering & Operational Excellence\nEnforce rigorous engineering standards, including Test-Driven Development (TDD), robust CI/CD pipelines, and DevSecOps practices.\n\nDrive a culture of Full Lifecycle Ownership, where the team is responsible for the design, security, deployment, and operational health of its services.\n\nEstablish and monitor key performance indicators (KPIs) for system health, code quality, delivery velocity, model performance, data quality, adoption, and realized business value.\n\nArchitectural Design\nArchitect and oversee the development of cloud-native, microservices-based systems designed for global scale, multi-tenancy, and high-performance transactional processing.\n\nDesign interoperable data and AI architectures that support batch and real-time inference, event-driven workflows, APIs, observability, and secure integration with enterprise platforms.\n\nPeople Leadership & Talent Development\nCultivate a high-performing, diverse team of Software Engineers, Product Managers, Data Engineers, Data Scientists, and ML Engineers through active coaching, mentorship, and career pathing.\n\nFoster a culture of psychological safety and continuous learning, utilizing blameless retrospectives and regular feedback loops to drive team growth.\n\nIdentify and close skill gaps within the team to keep pace with emerging technologies and industry trends.\n\nStrategic Collaboration\nAct 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.\n\nInfluence stakeholders across the organization to adopt modern engineering practices, responsible AI controls, reusable data products, and standardized service contracts.\n\nTechnical Execution (Hands-on)\nMaintain 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.\n\nLead 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.\n\nExperience: 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.\n\nAI/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.\n\nProduct 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.\n\nEducation: Undergraduate degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Statistics, Operations Research, or a related quantitative field.\n\nCertifications: 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.\n\nCloud Expertise: 4+ years of experience delivering production solutions on Google Cloud Platform (GCP), including cloud-native application and data/AI services.\n\nTechnical Depth: Expertise in microservices, cloud-native architectures, event-driven architectures, APIs, Domain-Driven Design (DDD), distributed systems, and secure enterprise integration.\n\nAI/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.\n\nData Engineering: Experience with data architectures, data pipelines, data quality, governance, metadata and lineage, feature stores, batch and streaming data, and analytics platforms.\n\nMLOps / 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.\n\nResponsible AI: Experience applying secure and responsible AI practices, including privacy, transparency, explainability, bias and fairness assessment, human oversight, access controls, risk management, and auditability.\n\nTechnology 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.\n\nEngineering Excellence: Thorough knowledge of multi-threading, concurrency, parallel processing, DevSecOps, test automation, and monitoring tools such as Dynatrace or Google Cloud Monitoring.\n\nDeveloper Experience: Experience increasing developer productivity by integrating AI agents, coding assistants, reusable platform capabilities, or AI skills into the development lifecycle.\n\nLeadership 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.\n\nCommunication & 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.\n\nNice to Have\nAdvanced degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Statistics, Operations Research, or a related field.\n\nExperience with Vertex AI, BigQuery, Feature Store, Gemini or other foundation-model platforms, vector search, retrieval-augmented generation (RAG), agentic workflows, and evaluation frameworks.\n\nExperience building reusable enterprise platforms and underlying services for data, analytics, and AI capabilities.\n\nExperience with forecasting, supply chain, order fulfillment, demand planning, optimization, or decision intelligence products.\n\nAbility to translate product roadmaps into manageable features through quarterly scoping sessions and assist product teams directly with technical blockers.\n\nProven ability to identify and mitigate delivery, data, model, security, adoption, and operational risks while assessing overall product health and prompting timely decisions.\n\nStrong understanding of business priorities and technical feasibility to prioritize platform backlogs and manage dependencies.\n\nExperience with Lean methodology, eXtreme Programming (XP), Agile product management, and Human-Centered Design.\n\nExperience championing modern software, data, AI/ML, product, and responsible AI practices within a large organization.","description_format":"text","description_chars":10879,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Automotive Manufacturing","Leasing, Auto & Equipment 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