Description

• Define and drive the organization-wide AI/GenAI roadmap, strategy, reference architectures and technology standards.
• Lead the architecture and implementation of enterprise AI solutions spanning classical machine learning, deep learning, Generative AI, LLMs, RAG, AI Agents and Agentic AI for retail and digital commerce.
• Provide hands-on technical leadership across the full AI lifecycle: data preparation, feature engineering, model development, training/fine-tuning, evaluation, deployment, inference and continuous improvement.
• Build and continuously evolve the organization's AI Center of Excellence (CoE), including reusable patterns, reference implementations and engineering playbooks.
• Establish enterprise standards for AI/ML architecture, prompt/context engineering, model evaluation, guardrails, security, governance, responsible AI and production readiness.
• Lead the transition of AI/ML/GenAI POCs and MVPs into scalable, secure, reliable and production-ready systems.
• Build reusable AI frameworks, accelerators, platforms and engineering standards that support both predictive ML and GenAI workloads.
• Evaluate and integrate foundation models including OpenAI/Azure OpenAI, Anthropic, Gemini, Llama and other models, and select appropriate ML/deep learning approaches when LLMs are not the best fit.
• Design MLOps/LLMOps practices for model and prompt versioning, experiment tracking, CI/CD, model registry, automated evaluation, deployment, monitoring, drift detection and rollback.
• Drive AI application performance, scalability, latency, observability, reliability, quality and cost optimization across training and inference workloads.
• Lead AI technical training and capability development across the organization, including structured learning programs for architects, data scientists, ML engineers, developers and engineering teams.
• Mentor and guide senior AI/ML, GenAI, architecture and software engineering teams, while remaining hands-on in critical architecture, prototyping and engineering decisions.
• Partner with CXOs and retail clients to identify AI/GenAI opportunities, assess technical feasibility and translate business problems into measurable AI solutions.
• Lead pre-sales, solutioning, RFP/RFI responses, proposals, estimations, presentations and SOW development for AI/ML/GenAI engagements.
• Conduct architecture, model and technical design reviews and provide technical leadership across AI engagements.

What You Know:

• 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.

Education:

BTech/BE or MTech/MS in Computer Science, Information Technology, Artificial Intelligence, Machine Learning or a related field. Advanced degree is preferred

Key Skills

Education

Bachelor's degree