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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