• 15+ years of software engineering/technology experience, including substantial hands-on experience architecting and engineering enterprise AI/ML/GenAI systems. • Strong foundation in machine learning, including supervised and unsupervised learning, feature engineering, model selection, evaluation, experimentation and optimization. • Strong deep learning expertise with practical experience in neural network architectures such as transformers, CNNs, sequence models and representation/embedding learning. • Strong expertise in Generative AI, LLMs, RAG, AI Agents and Agentic AI, including tool use, orchestration, memory/context strategies and evaluation. • Hands-on proficiency in Python and modern AI/ML frameworks such as PyTorch, TensorFlow/Keras, scikit-learn and related model development libraries. • Proven experience deploying AI/ML/GenAI solutions to production, including real-time and batch inference, API/model serving, scalability, resilience and performance optimization. • Strong experience with MLOps/LLMOps, including experiment tracking, model/prompt versioning, CI/CD, model registries, automated testing/evaluation, monitoring, drift detection and lifecycle management. • Experience with AWS, Azure and/or GCP, including AI/ML platforms and services such as Azure Machine Learning/Azure OpenAI, AWS SageMaker/Bedrock or Google Vertex AI. • Strong understanding of embeddings, vector databases, retrieval techniques, prompt/context engineering, LLM evaluation and grounding/citation approaches. • Experience with data and feature pipelines, distributed processing and the data engineering patterns required to support production AI systems. • Experience with Docker, Kubernetes, Git, CI/CD, APIs/microservices and modern software engineering practices for production AI applications. • Strong understanding of AI security, privacy, governance, explainability, bias/fairness, model risk and responsible AI. • Proven experience building enterprise AI platforms, reusable accelerators, shared AI services or AI Centers of Excellence. • Ability to make sound architecture trade-offs across classical ML, deep learning and GenAI approaches based on accuracy, latency, cost, maintainability, data availability and business value. • Strong retail, digital commerce or customer experience domain experience preferred. • Excellent communication, consulting, stakeholder management and client-facing skills, with the ability to engage effectively with both CXO-level stakeholders and hands-on engineering teams.