Lead AI Engineer SFO, CA, Retail Industry Experience

Key Responsibilities We are seeking an experienced Lead AI Engineer to lead strategic AI initiatives at a customer location. This role combines deep technical expertise in Generative AI, Agentic AI, Machine Learning, and AI Platform Engineering with the ability to engage senior stakeholders, define AI roadmaps, architect enterprise solutions, and lead delivery teams. The successful candidate will act as the primary AI technical lead, driving the design, development, deployment, and operationalization of business-critical AI solutions while ensuring alignment with customer objectives and enterprise standards. Required Qualifications Minimum 8+ years of experience in machine learning engineering and AI development, including 2+ years in a technical lead or team lead capacity Proven track record of leading ML/AI projects end-to-end and mentoring engineers Demonstrated experience in client-facing or consulting roles, including managing senior stakeholders and serving as a technical advisor Strong stakeholder-management skills: gathering and clarifying requirements, setting expectations, managing scope, and building long-term trust with customers Excellent verbal and written communication, with the ability to present complex technical concepts and trade-offs to non-technical and executive audiences Deep expertise in machine learning algorithms and techniques: supervised/unsupervised learning, deep learning, reinforcement learning, etc. Solid experience in natural language processing (NLP): language models, text generation, sentiment analysis, etc. Proven understanding of generative AI concepts: text/image/audio synthesis, diffusion models, transformers, etc. Expertise in Agentic AI and building production-grade real-world applications using it Hands-on experience with Agentic AI frameworks such as LangGraph, ADK, Autogen, etc. Hands-on experience developing and deploying generative AI applications (text generation, conversational AI, image synthesis, etc.) Strong experience in MLOps and ML model deployment pipelines, including defining standards and best practices for a team Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, etc. Knowledge of cloud platforms (AWS, Google Cloud Platform, Azure) and tools for scalable ML solution deployment Experience with data processing, feature engineering, and model training on large datasets Familiarity with responsible AI practices, AI ethics, model governance, and risk mitigation Strong grasp of software engineering best practices and their application to ML systems Experience driving delivery within an agile development environment Exposure to tools/frameworks for monitoring and analyzing ML model performance and data accuracy Strong problem-solving and analytical abilities Bachelor's or master s degree in computer science, AI, Statistics, Math, or related fields Willingness and ability to work on-site at the client's office Proven experience working in an onshore/offshore delivery model coordinating distributed teams across geographies and time zones Responsibilities Client Engagement & Stakeholder Management Serve as the primary technical point of contact and trusted advisor at the client's office Engage senior client stakeholders to understand business goals, gather requirements, and shape the AI/ML roadmap Manage scope, timelines, expectations, and risks; proactively communicate progress, trade-offs, and blockers Translate ambiguous business problems into well-defined technical solutions and clear delivery plans Present designs, outcomes, and recommendations to technical and executive audiences; influence decisions and build consensus Identify opportunities to expand value delivered to the customer Technical Leadership & Strategy Define the technical vision, architecture, and roadmap for ML/AI initiatives across the client's domains Set engineering standards and best practices for model development, MLOps, and responsible AI Evaluate and select frameworks, tools, and platforms; make build-vs-buy and design trade-off decisions balancing performance, scalability, cost, and reliability Team Leadership & Mentorship Lead, mentor, and grow a team of ML/AI engineers; conduct design and code reviews Plan, prioritize, and delegate work; track progress and remove blockers Foster a culture of technical excellence, knowledge-sharing, and continuous learning Delivery & Execution Design, develop, and optimize machine learning models for applications across different domains Oversee the building of NLP pipelines for tasks like text generation, summarization, translation, etc. Drive the development and deployment of cutting-edge generative AI and Agentic AI applications Establish and govern MLOps practices: model training, evaluation, deployment, monitoring, and maintenance Integrate ML capabilities into existing products or lead the creation of new AI-powered applications Ensure robust data mining, cleaning, preparation, and augmentation for training reliable ML models Own model performance, scalability, and reliability across the production lifecycle Innovation Continuously research, evaluate, and introduce state-of-the-art AI/ML algorithms and techniques Champion responsible for AI, model governance, and risk mitigation across the team's work