Software Engineering Manager

Role overview

Product and AI/ML leadership

• Define AI/ML and analytics strategy and prioritize opportunities across forecasting, order generation, decision support, anomaly detection, optimization, and automation.
• Lead AI/ML products from discovery and business case through experimentation, MVP, launch, adoption, and continuous improvement.
• Set product metrics, model targets, experimentation methods, and adoption measures; assess emerging AI technologies and build/buy/partner options.
• Partner with stakeholders on multi-year roadmaps, Agile MVP delivery, human-centered design, launch readiness, training, change management, and benefit tracking.

Architecture and engineering

• Lead Order Generation technology, data architecture, AI/ML capabilities, enterprise integration, and technical strategy.
• Architect cloud-native microservices for global scale, multi-tenancy, high-performance transactions, batch and real-time inference, event-driven workflows, APIs, and secure integrations.
• Guide trusted data, MLOps/LLMOps, model monitoring, drift detection, retraining, rollback, auditability, explainability, fairness, privacy, and human oversight.
• Enforce TDD, CI/CD, DevSecOps, full lifecycle ownership, and KPIs for system health, code quality, delivery, models, data, adoption, and business value.
• Review architecture, code, model and data designs; resolve technical blockers and prototype high-risk concepts.

People and collaboration

• Coach and develop engineers, product managers, data engineers, data scientists, and ML engineers; foster psychological safety and continuous learning.
• Collaborate with cloud, data and AI, cybersecurity, responsible AI, SRE, and DevOps experts and influence adoption of modern practices.

Required qualifications

• 10+ years of progressive software engineering, digital product, data, or AI/ML delivery experience, including significant engineering and product leadership.
• Experience scaling AI/ML products and defining strategy, business cases, roadmaps, MVPs, launch plans, adoption strategies, and value metrics.
• Undergraduate degree in computer science, engineering, data science, AI, statistics, operations research, or a related quantitative field.
• 4+ years delivering production solutions on GCP; expertise in cloud-native, microservices, event-driven, API, distributed-system, and secure integration design.
• Knowledge of ML methods, forecasting, optimization, anomaly detection, feature engineering, evaluation, experimentation, and production inference.
• Experience with data architecture, pipelines, governance, quality, lineage, feature stores, batch/stream processing, and analytics platforms.
• Hands-on model lifecycle experience, including CI/CD, experimentation, registry, validation, deployment, monitoring, drift, and retraining.
• Experience applying responsible AI controls and hands-on experience with Java, Angular, Python, SQL, Terraform, Postgres, APIGEE, Kubernetes, Docker, serverless, and containers.

Preferred and additional qualifications

• GCP Professional Cloud Architect, Professional Machine Learning Engineer, equivalent certification, or commitment to obtain a relevant certification within six months; listed GCP certifications are a plus.
• Vertex AI, BigQuery, Dataflow, Pub/Sub, foundation models, vector search, RAG, agentic workflows, and evaluation frameworks are preferred or advantageous.
• Advanced degree, reusable enterprise data/AI platforms, order fulfillment or supply-chain products, Lean, XP, Agile product management, and Human-Centered Design are advantageous.