LLM Engineer (AI)

A bleeding-edge, hyper-growth Generative AI & Identity Security startup that is redefining enterprise security operations.

Backed by substantial multi-million dollar funding from legendary tier-one venture capital giants the company was founded by a highly prominent serial entrepreneur with multiple massive successful exits under his belt.

The company has engineered a revolutionary Multi-Agent LLM platform that autonomously orchestrates complex identity lifecycles and dynamically remediates security events using advanced natural language workflows achieving automation paradigms previously deemed impossible.

Composed of a rapidly expanding team of elite professionals, located in a premier, central tech district (exceptionally accessible, situated within short walking distance of a main train line), and operating on an on-site office model.

Position Overview

• Senior AI / Machine Learning Engineer joining a core, elite AI division tasked with architecting the cognitive logic, system-wide behavior monitoring frameworks, and execution networks of autonomous agents.
• Building and optimizing sophisticated Multi-Agent architectures, Retrieval-Augmented Generation (RAG) loops, and complex context-engineering data pipelines to ensure secure, real-time enterprise operations.
• Working at the true frontier of GenAI technology, integrating a mix of open-source models and proprietary APIs, while managing model orchestration layers and training dynamics.

Requirements

• 5+ years of proven professional experience operating as an ML Engineer, AI Engineer, or Data Scientist within complex product environments - Strictly Mandatory
• Deep, production-grade programming mastery in Python, including the design of heavy, data-driven application logic - Mandatory
• Hands-on, production-level engineering experience with LLMs (Large Language Models), Multi-Agent frameworks (e.g., LangGraph/LangChain), RAG, and Vector Databases - Strictly Mandatory
• Practical, hands-on familiarity with deep learning and model-hosting ecosystems, such as PyTorch, Hugging Face, or closely related frameworks - Mandatory
• Practical experience deploying and managing AI pipelines over modern containerized cloud infrastructure (AWS/GCP/Azure, Docker, Kubernetes) - Significant Advantage