Gen AI Lead Engineer - SVP

We are seeking a dynamic and innovative Gen AI Lead to spearhead the development and integration of Generative AI capabilities across our enterprise-wide Controls Technology platform. As the Gen AI Lead, you will be responsible for building, implementing, and optimizing AI-driven solutions to enhance operational efficiencies, automate decision-making, and drive strategic insights. This role requires both hands-on expertise in AI/ML and leadership skills to build and lead a high-performing AI team.

Key Responsibilities:

• Lead Generative AI Strategy: Define and implement a comprehensive AI strategy aligned with enterprise-wide goals, focusing on innovation in the Controls Technology domain.

• Build & Lead a High-Performing Team: Hire, mentor, and manage a team of AI specialists, ensuring the acquisition and retention of top talent in the AI/ML space.

• Drive AI Innovation: Collaborate with internal stakeholders to identify business challenges that can be solved through AI, creating scalable solutions using Generative AI technologies.

• AI System Development: Oversee the design, development, and deployment of AI models and algorithms, ensuring solutions are robust, efficient, and scalable.

• Cross-functional Collaboration: Work closely with the Data Mesh, Cloud Architecture, and broader tech teams to integrate AI models into existing and future architectures.

• Stay Current on AI Trends: Continuously monitor the latest AI trends and technologies to ensure the organization remains at the cutting edge of Gen AI innovations.

• Ensure Ethical AI Use: Ensure all AI initiatives comply with data privacy, ethical AI standards, and corporate governance policies.

Required Technical Skills:

• Large Language Models (LLMs) & Fine-Tuning: Deep knowledge of LLMs and advanced fine-tuning techniques and proficient in Parameter-Efficient Fine-Tuning (PEFT) methods, including LoRA, QLoRA, Adapter Tuning, and Prefix Tuning. Experience with full fine-tuning, instruction tuning strategies, and cutting-edge agentic AI techniques such as Reinforcement Learning from Human Feedback (RLHF) and multi-task learning is essential.

• Model Optimization: Expertise in model compression and quantization methods and be skilled in techniques like AWQ and GPTQ, particularly GPTQ-for-LLaMA. Proficiency in using optimized inference engines such as vLLM, DeepSpeed, and FP6-LLM is crucial for maximizing model performance and efficiency.

• Prompt Engineering: Adept at prompt engineering, demonstrating proficiency in various techniques and best practices. Familiarity with tools and frameworks that facilitate effective prompt design is necessary to guide the team in creating powerful and efficient AI interactions.

• Retrieval-Augmented Generation (RAG): Advanced knowledge of RAG techniques is required, including expertise in hybrid search methods, multi-vector retrieval, Hypothetical Document Embeddings (HyDE), self-querying, query expansion, re-ranking, and relevance filtering. This knowledge will be crucial in developing sophisticated AI systems that can leverage external knowledge effectively.

• Machine Learning Frameworks and Cloud Computing: Proficiency in TensorFlow, PyTorch, and high-level APIs like Keras is essential. Knowledge of distributed training and parallel processing frameworks is also required to handle large-scale AI projects efficiently.

• Natural Language Processing (NLP) and AI Deployment: Advanced NLP skills, including Named Entity Recognition (NER), Dependency Parsing, Text Classification, and Topic Modeling. Experience with Transfer Learning, Few-shot, and Zero-shot learning paradigms is crucial. Expertise in containerization (Docker), orchestration (Kubernetes), and experience with CI/CD pipelines for AL/ML model deployment.

• Data Science, Engineering, and API Development: Strong proficiency in data preprocessing, feature engineering, and handling large-scale datasets is required. Experience with real-time AI applications and streaming data processing is valuable. Expertise in designing and implementing RESTful API