AI/ML Lead LLM & RAG Systems

About the job

About The Role We are seeking a hands-on AI/ML Lead to architect and build production-grade AI systems leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, and scalable cloud infrastructure. This role combines deep technical expertise with team leadership and strategic ownership of AI initiatives. Key Responsibilities AI Architecture & Development: • Architect and build scalable AI-powered applications. • Design, implement, and optimize RAG systems for production use. • Integrate and manage LLM APIs (OpenAI, Claude, and open-source models). • Define AI system architecture and long-term technical roadmap. • Ensure system performance, scalability, security, and cost efficiency. Leadership & Team Management: • Lead, mentor, and grow a team of 25 AI engineers. • Own sprint planning, task allocation, and technical decision-making. • Conduct code reviews and enforce engineering best practices. • Ensure timely and high-quality delivery of AI solutions. Deployment & Infrastructure: • Oversee deployment and infrastructure management on AWS or Azure. • Manage containerization and cloud-native environments. • Optimize systems for reliability and operational excellence. Technical Requirements • Strong expertise in Python. • Proven hands-on experience implementing RAG architectures. • Experience working with LLM APIs (OpenAI, Claude, open-source models). • Proficiency in LangChain and/or LlamaIndex. • Experience with vector databases (Pinecone, Weaviate, FAISS, etc.). • Strong knowledge of REST APIs and PostgreSQL. • Experience with Docker and cloud deployment (AWS / Azure). • Strong system design and architectural skills. • Prior experience leading technical teams. Preferred Qualifications • Experience building AI products in production environments. • Understanding of AI system cost optimization and monitoring. • Experience with MLOps best practices.

Requirements

• Python
• LLM APIs
• RAG architectures
• Cloud Deployment

Preferred Technologies

• Python
• LLM APIs
• RAG architectures
• Cloud Deployment