AI Architect – Agentic Systems (LLM & Multi‑Agent Solutions)

About the role

We are seeking a highly skilled AI Architect to design and lead production‑grade multi‑agent systems that integrate large language models (LLMs) with enterprise data, APIs, and business workflows. This role goes beyond prompt engineering, focusing on building robust, scalable AI solutions that automate complex processes across the organization.

Key responsibilities

• Design multi‑agent architectures, including task decomposition, orchestration, and coordination patterns.
• Define interaction models between LLM‑powered agents and enterprise data platforms, APIs, and operational tools.
• Lead end‑to‑end delivery from proof‑of‑concept to production, addressing model selection, cost, security, privacy, and responsible AI safeguards.
• Establish observability, tracing, feedback loops, and control mechanisms for agent behavior.
• Design memory strategies for short‑term, long‑term, and contextual grounding.
• Collaborate with data, platform, and engineering teams to embed AI into core systems.
• Mentor and train teams on best practices for scalable, reliable AI deployments.

Required profile

• Strong background in software architecture and distributed systems.
• Hands‑on experience building complex LLM‑based applications and workflow orchestration.
• Deep understanding of Retrieval‑Augmented Generation (RAG) architectures and retrieval quality.
• Solid grasp of Transformer fundamentals, tokenization, and model training nuances.
• Experience with multi‑agent frameworks such as LangChain, Semantic Kernel, or CrewAI.
• Familiarity with cloud AI platforms (AWS, Azure, Google Cloud) and generative AI lifecycle management.

Required skills

• Software architecture
• Distributed systems
• Large Language Models (LLM)
• Retrieval‑Augmented Generation (RAG)
• Transformer architecture
• LangChain
• Semantic Kernel
• CrewAI
• Amazon Bedrock
• Google Vertex AI
• Azure AI Foundry
• AWS
• Azure
• Google Cloud Platform (GCP)