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, Autogen, Crew.ai, LangChain, LlamaIndex, and Hugging Face Transformers.

Familiarity with Gen AI APIs (OpenAI, Gemini, Claude) and version control systems like Git. AI Compliance & Guardrails: Knowledge of AI compliance frameworks and best practices. Experience implementing guardrails to ensure ethical AI usage and mitigate risks (e.g., Microsoft's AI Guidance Framework).

Required Leadership & Soft Skills Delivery Leadership: Proven ability to lead and deliver complex, large-scale technical projects from concept to production. Program Management: Expertise in Agile/Scrum methodologies, project planning, resource allocation, and risk management.

Strategic Execution: Capacity to translate high-level AI strategy into a concrete, actionable delivery plan and execute it effectively. Stakeholder Management: Exceptional ability to manage expectations, communicate complex technical topics clearly, and build strong relationships with both technical and non-technical stakeholders.

Pragmatic Innovation: A passion for applying cutting-edge AI technologies to solve real-world business problems in a practical and efficient manner. Problem Solving: Proactive and analytical mindset to overcome technical and logistical challenges in a fast-paced environment