GenAI Lead

Information Security Responsibilities

• Promote and enforce awareness of key information security practices, including acceptable use of information assets, malware protection, and password security protocols
• Identify, assess, and report security risks, focusing on how these risks impact the confidentiality, integrity, and availability of information assets
• Understand and evaluate how data is stored, processed, or transmitted, ensuring compliance with data privacy and protection standards (GDPR, CCPA, etc.)
• Ensure data protection measures are integrated throughout the information lifecycle to safeguard sensitive information

Key Responsibilities

• Lead Agentic Application Development: Design, build, and deploy autonomous AI agents capable of planning, reasoning, and interacting with external systems to fulfill complex tasks. 
• Oversee GenAI Product Architecture: Guide the technical direction across multiple AI-driven products, ensuring quality, consistency, and scalability in implementation. 
• Develop RAG Systems: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines that combine structured retrieval with generative models for high-accuracy answers. 
• Deliver Production-Grade Code: Write scalable, maintainable Python code for deploying machine learning and reasoning systems in production. 
• Prompt Engineering Optimization: Refine prompt strategies and retrieval flows to improve performance and reliability of LLM-based systems. 
• Data Preparation & Analysis: Analyze structured and unstructured data sources, creating high-quality inputs to power model pipelines. 
• Mentor & Collaborate: Work closely with engineers, data scientists, and product teams to align technical strategy with business goals, and to upskill teammates on GenAI tooling and practices. 
• Maintain & Monitor Systems: Ensure high availability and performance of deployed solutions, with robust observability and feedback loops. 
• Stay Current: Track the latest research and tooling in LLMs, autonomous agents, retrieval systems, and reasoning frameworks—and bring these into practice. 

Technical Experience

• Proven experience developing and deploying agentic applications into production. 
• Strong proficiency in Python. 
• Hands-on experience with deploying machine learning models and LLM-based pipelines at scale. 
• Familiarity with frameworks like LlamaIndex, LangChain, or Semantic Kernel. 
• Deep knowledge of MLOps/LLMOps best practices and CI/CD workflows. 
• Experience with containerization using Docker. 
• Proficient with AWS services related to AI and scalable compute (e.g., S3, Lambda, SageMaker, ECS, EKS). 
• Working knowledge of Kubernetes for orchestration.

Soft Skills

• Excellent communication and leadership abilities. 
• Self-driven, independent, and proactive. 
• Strong mentorship skills and ability to align technical vision across teams. 
• Detail-oriented with a systematic approach to problem-solving. 

Nice-to-Have Qualifications

• Strong foundation in natural language processing (NLP). 
• Familiarity with reasoning algorithms, planning systems, or multi-agent frameworks. 
• Exposure to GitOps workflows for infrastructure and deployment. 
• Prior experience building multi-modal AI applications or tools that interface with third-party APIs and plugins. 

Education Background

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related technical field.