Software Engineering Manager

Ford Motor Company

. Serve as the primary technical authority for the Order Generation product suite .

Posted 9/23/2026full-timeChennai • IndiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AI/ML product strategy, cloud-native architectures, and data analytics, with a strong focus on leading cross-functional teams and delivering measurable business outcomes. Proficient in establishing product vision, managing complex integrations, and applying responsible AI practices.

Highest-signal resume keywords

AI/ML Product StrategyCloud-Native ArchitectureData AnalyticsMicroservicesGoogle Cloud Platform (GCP)

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Hard Skills

JavaPythonSQLAngularTerraformKubernetesDockerAPIGEEData ArchitectureCI/CD

Soft Skills

LeadershipMentorshipCommunicationCollaborationInfluence

Tools & Technologies

DynatraceGoogle Cloud MonitoringData PipelinesExperiment TrackingModel Registry

Certifications & Qualifications

Industry Certifications in Software EngineeringCloudDataAI/ML

Industry Keywords

AI/ML SolutionsDigital Product DeliveryData GovernanceOperational KPIsHuman-Centered Design

Tech Stack

Tools & technologies

AngularCloudCyber SecurityDistributed SystemsDockerGoogle Cloud PlatformJavaKubernetesMicroservicesPostgresPythonSQLTerraform

About the role

Key responsibilities & impact

Serve as the primary technical authority for the Order Generation product suite
Define the technology stack, data architecture, AI/ML capabilities, and architectural patterns
Lead cross-functional teams through complex integrations and manage ecosystem dependencies
Translate business requirements into technical strategies aligned with Ford enterprise standards
Define and execute AI/ML and data analytics product strategy
Identify, evaluate, and prioritize AI/ML opportunities across forecasting, order generation, decision support, anomaly detection, optimization, and workflow automation
Lead AI/ML products from discovery and business-case development through experimentation, MVP validation, industrialization, launch, adoption, and continuous improvement
Partner with Data Science, Data Engineering, Product, Architecture, Cybersecurity, Legal, Privacy, and business teams
Establish product metrics, experimentation methods, model performance targets, and adoption measures
Monitor emerging technologies and assess build, buy, or partner opportunities
Define and execute a multi-year product vision and roadmap for optimized order forecasting and generation
Champion Agile delivery, MVPs, Human-Centered Design, launch planning, adoption, training, change management, and feedback loops
Guide MLOps and LLMOps practices including experimentation, model registry, testing, deployment, monitoring, drift detection, retraining, rollback, and auditability
Define controls for model quality, explainability, bias and fairness, privacy, security, human oversight, and responsible use
Enforce Test-Driven Development, CI/CD, DevSecOps, Full Lifecycle Ownership, and operational KPIs
Architect cloud-native, microservices-based systems for global scale, multi-tenancy, and high-performance transactional processing
Design interoperable data and AI architectures supporting batch and real-time inference, event-driven workflows, APIs, observability, and secure enterprise integration
Cultivate and develop a diverse team through coaching, mentorship, career pathing, psychological safety, and continuous learning
Collaborate with Cloud Infrastructure, Data & AI, Cybersecurity, Responsible AI, SRE, and DevOps teams
Influence adoption of modern engineering practices, responsible AI controls, reusable data products, and standardized service contracts
Participate in architecture and code reviews, resolve technical blockers, review model and data design decisions, and prototype emerging technologies

Requirements

What you’ll need

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
Demonstrated experience strategizing, developing, launching, and scaling AI/ML-based products that address business requirements and deliver measurable operational or customer outcomes
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
Undergraduate degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Statistics, Operations Research, or a related quantitative field
Industry certifications relevant to software engineering, cloud, data, or AI/ML, or commitment to obtain them within 6 months
4+ years of experience delivering production solutions on Google Cloud Platform (GCP), including cloud-native application and data/AI services
Expertise in microservices, cloud-native architectures, event-driven architectures, APIs, Domain-Driven Design (DDD), distributed systems, and secure enterprise integration
Strong working knowledge of supervised and unsupervised learning, time-series forecasting, optimization, anomaly detection, feature engineering, model evaluation, experimentation, and production inference patterns
Experience with data architectures, data pipelines, data quality, governance, metadata and lineage, feature stores, batch and streaming data, and analytics platforms
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
Experience applying secure and responsible AI practices, including privacy, transparency, explainability, bias and fairness assessment, human oversight, access controls, risk management, and auditability
Hands-on experience with Java, Angular, Python, SQL, Terraform, Postgres, APIGEE, Kubernetes, Docker, serverless technologies, and containerization
Thorough knowledge of multi-threading, concurrency, parallel processing, DevSecOps, test automation, and monitoring tools such as Dynatrace or Google Cloud Monitoring
Experience increasing developer productivity by integrating AI agents, coding assistants, reusable platform capabilities, or AI skills into the development lifecycle
Proven ability to lead large-scale transformations, apply systems thinking, create psychologically safe teams, influence complex decisions, and mentor technical talent
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