Generative AI - Group Manager - Senior Vice President

## We are seeking a results-driven **Generative AI group manager** with end-to-end experience for execution and deployment of cutting-edge Generative AI solutions for Services Tech AI team . 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 someone who combines deep technical expertise in generative AI with a proven track record of successfully delivering complex technology projects. **Key Responsibilities** - **GenAI Delivery Leadership:** Execute the delivery roadmap for generative AI projects, ensuring alignment with business objectives and timelines. Manage the 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 Keras. Knowledge of distributed training, parallel processing, and extensive hands-on experience with AWS services for AI/ML. - **Natural Language Processing (NLP) and AI Deployment:** Advanced NLP skills (NER, Dependency Parsing, Text Classification, Topic Modeling). Experience with Transfer Learning, Few-shot, and Zero-shot learning. Expertise in containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for MLOps. - **Data Science, Engineering, and API Development:** Strong proficiency in data preprocessing, feature engineering, and handling large-scale datasets. Experience with real-time AI applications, streaming data, and designing RESTful APIs for model integration. - **Generative AI Tools & Platforms:** Experienced with LangGraph, Autoge…