Gen AI Delivery Lead - SVP

We are seeking a results-driven Generative AI Delivery lead to lead the end-to-end execution and deployment of cutting-edge Generative AI solutions across our enterprise-wide Controls Technology platform. In this role, you will be responsible for translating AI strategy into tangible, production-ready capabilities that enhance operational efficiencies and drive business value. We're looking for a leader who combines deep technical expertise in generative AI with a proven track record of successfully delivering complex technology projects. 

Key Responsibilities 

• GenAI Delivery Leadership: Define and execute the delivery roadmap for generative AI projects, ensuring alignment with business objectives and timelines. Manage the entire project lifecycle from ideation and scoping to deployment and post-launch support. 

• Team Leadership & Mentorship: Build, mentor, and manage a high-performing team of AI engineers and specialists. Foster a culture of execution, collaboration, and continuous improvement to successfully deliver on the AI roadmap. 

• End-to-End Solution Delivery: Oversee the design, development, and deployment of robust, scalable, and production-ready GenAI models. Ensure all solutions meet rigorous performance, security, and quality standards before and after deployment. 

• Stakeholder & Program Management: Serve as the primary point of contact for GenAI delivery. Manage stakeholder expectations, communicate project progress, identify and mitigate risks, and ensure on-time and on-budget delivery. 

• Cross-Functional Partnership: Collaborate closely with Data Mesh, Cloud Architecture, MLOps, and business unit teams to ensure the seamless integration and operationalization of AI models into our existing technology ecosystem. 

• Technical Excellence & Best Practices: Drive the adoption of best practices in software development (CI/CD), MLOps, and project management (Agile/Scrum) within the AI team to ensure efficient and repeatable delivery. 

• Governance & Ethical Deployment: Implement and enforce robust governance and ethical AI frameworks throughout the delivery process, ensuring compliance with data privacy standards and corporate policies. 

Required Technical Skills 

• Large Language Models (LLMs) & Fine-Tuning: Deep knowledge of LLMs and advanced fine-tuning techniques. Proficient in Parameter-Efficient Fine-Tuning (PEFT) methods (LoRA, QLoRA, Adapter Tuning, Prefix Tuning), full fine-tuning, instruction tuning, and agentic AI techniques (RLHF, multi-task learning). 

• Model Optimization: Expertise in model compression and quantization methods (AWQ, GPTQ, GPTQ-for-LLaMA). Proficiency with optimized inference engines such as vLLM, DeepSpeed, and FP6-LLM. 

• Prompt Engineering: Adept at advanced prompt engineering techniques and best practices. Familiarity with frameworks that facilitate effective prompt design and management. 

• Retrieval-Augmented Generation (RAG): Advanced knowledge of RAG techniques, including hybrid search, multi-vector retrieval, Hypothetical Document Embeddings (HyDE), self-querying, query expansion, re-ranking, and relevance filtering. 

• Machine Learning Frameworks and Cloud Computing: Proficiency in TensorFlow, PyTorch, and