GenAI Lead

Job Description

Essential functions 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. Would be a plus Proven experience leading small AI/ML teams, with a track record of delivering projects and mentoring team members. Knowledge of data security and compliance frameworks, as well as experience with responsible AI practices (ethical AI, bias mitigation, data privacy). Background in financial, payments, or security-related AI applications, giving you insight into the challenges and standards of the fintech industry.