Job Description

This is a remote position.

We are seeking an AI Engineer with deep expertise in Large Language Models (LLMs), Generative AI, and Agentic AI. In this role, you will work on real-world applications such as autonomous agents, retrieval-augmented generation (RAG), multi-agent collaboration, and intelligent copilots.

As a specialist in multi-agent systems, you will design, develop, optimize, and deploy intelligent agents capable of reasoning, planning, and collaborating within coordinated environments. You’ll collaborate cross-functionally to deliver production-ready AI solutions powered by the latest LLM and agentic frameworks.

Requirements

Responsibilities

Design and implement agentic AI systems with capabilities in reasoning, planning, memory, and contextual tool usage.
Build and maintain multi-agent orchestration systems with role-based reasoning, dynamic task allocation, and resilient communication protocols.
Develop RAG pipelines to integrate enterprise knowledge into LLM workflows.
Build performant, scalable, and efficient multi-agentic systems.
Embed autonomous agents into real-world applications and digital products.
Optimize for performance, scalability, and robustness in production environments.
Leverage frameworks like LangChain and LangGraph for agent orchestration and memory management.

Basic Qualifications

Proven ability to quickly prototype, iterate, and deploy AI-powered features.
Strong analytical, communication, and collaboration skills.
Passion for staying current with GenAI tools, LLM research, and best practices.
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
Proven minimum 1+ year of hands-on experience with Agentic AI and multi-agent systems in production or enterprise environments.
Proficient in Python and modern software engineering practices.
Practical experience with LangChain, LangGraph, and prompt engineering.
Experience with retrieval-augmented generation (RAG) pipelines.
Familiarity with agentic memory architectures and Model Context Protocol (MCP).
Comfortable working on Azure or other major cloud platforms.
Experience with Docker, Kubernetes, or similar containerization technologies.
Experience implementing guardrails for LLMs and agent-based systems.
Understanding of data governance principles and how they apply in AI system development.
Experience with evaluation metrics, performance tracing, and logging tools such as LangSmith or similar platforms.
Familiar with HIL (human in the loop)

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