LLM Engineer (AI)

A highly successful, growth-stage B2D (Business-to-Developer) infrastructure and cloud optimization enterprise.

The company develops an advanced, real-time resource management platform running on Kubernetes that empowers engineering and DevOps teams to maximize cloud performance while drastically driving down operational overhead.

By dynamically and autonomously allocating compute resources, the platform eliminates major cloud waste for prominent technology leaders and enterprise clients globally.

Backed by a substantial Series C capitalization from top-tier global and domestic venture capital funds, the company scales with a premier engineering roster heavily consisting of elite tech veterans and scale-up engineers.

Position Overview-

• AI & Agentic Systems Engineer joining an elite, specialized AI engineering squad reporting directly to the Director of AI.
• Designing, developing, and architecting autonomous AI Agents and the underlying multi-agent frameworks capable of orchestration inside complex infrastructure environments.
• Engineering production-grade backend systems and scalable AI pipelines using Python to handle real-world operational scale.
• Implementing advanced cognitive patterns, including Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), stateful orchestration, and high-performance vector databases (VectorDBs).
• Building robust evaluation loops, benchmarking criteria, and optimization layers to ensure multiple autonomous agents seamlessly cooperate as a unified, deterministic system.
• Core Domain & Ecosystem- Agentic AI, Multi-Agent Systems, Python Backend Engineering, LLM Integration, LangGraph, LangChain, RAG Architecture, Model Context Protocol (MCP), Vector Databases, Kubernetes & DevOps Ecosystem.

Requirements-

• 3 years of proven professional experience split between Backend Software Engineering and AI System Development – Mandatory
• Extensive, production-grade programming proficiency utilizing Python – Mandatory
• Hands-on engineering experience implementing, prompting, or fine-tuning Large Language Models (LLMs) and Generative AI frameworks – Mandatory
• Verifiable track record building, testing, and shipping AI, Machine Learning, or complex algorithmic systems directly into live Production environments – Mandatory
• Strong engineering-focused mindset with the architectural capacity to write clean, maintainable code supporting advanced automation workflows – Mandatory