GenAI Lead

• Design, train, fine-tune, and optimize local LLMs or other NLP models for PII detection across diverse data types (documents, databases, knowledge graphs, code, and other knowledge-sharing formats).
• Develop generative AI agents (on Autogen/Langgraph) for schema- and metadata-based PII detection to enhance identification of sensitive data.
• Work with cutting-edge AI frameworks (Ray, llama.cpp, ollama, vLLM, PyTorch) to deploy and scale models efficiently in a distributed environment.
• Implement and optimize AI/ML solutions on Azure cloud and on-premise infrastructure, ensuring high performance and reliability.
• Collaborate with data engineering, security, and compliance teams to align AI solutions with business needs and regulatory requirements.
• Lead a small team of AI engineers, providing mentorship, code reviews, and technical guidance to drive project success.
• Maintain and monitor model performance, retraining models on a quarterly or monthly basis to handle 50+ PB of evolving data and to improve accuracy over time.
• Ensure AI models follow best practices and compliance standards, adhering to security requirements and regulations (GDPR, CCPA, PCI DSS, etc.).

Qualifications

• Strong experience with AI frameworks such as Ray, llama.cpp, ollama, vLLM, and PyTorch for building and scaling LLM solutions.
• Expertise in LLM fine-tuning and prompt engineering, including techniques like Reinforcement Learning from Human Feedback (RLHF) to refine model outputs.
• Hands-on experience with AI model deployment in Azure cloud environments as well as on-premises servers.
• Familiarity with large-scale data (50+ PB) and distributed computing paradigms (e.g., using clusters or Ray) to handle massive datasets.
• Familiarity with MCP (Model Context Protocol) Servers and securing them.
• Strong programming skills in Python, with experience in machine learning frameworks and libraries.
• Ability to work cross-functionally with stakeholders in security, compliance, and data engineering to incorporate their requirements into AI solutions.
• Strong awareness if not implementation experience with Differential Privacy/Federated Learning.
• Excellent communication skills, with the ability to explain complex AI concepts and results to non-technical teams and leadership clearly.