JOB 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 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 up-to-date software, data, AI/ML, product, and responsible AI practices within a large organization.

📌 Software Engineering Manager (Chennai)
🏢 Ford Motor
📍 Chennai