Job Summary

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 workplace.

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.

📌 Software Engineering Manager (Chennai)
🏢 Ford Business Solutions
📍 Chennai