AI ARCHITECT - Role Overview

We are seeking an experienced AI Architect to lead the design, development, and production deployment of autonomous multi-agent systems. You will move beyond simple chatbots to build stateful, goal-oriented agentic workflows that can reliably execute complex business logic.

This role can be remote in Greece or Poland, or hybrid in our Dublin office.

Key Responsibilities

Architecture & System Design

Design multi-agent architectures (e.g., Supervisor-Worker, Hierarchical Teams) capable of breaking down complex user queries into sub-tasks.
Define the state management strategy to ensure agents retain context, memory, and user intent across long-running workflows.
Architect robust Retrieval-Augmented Generation (RAG) pipelines that allow agents to query proprietary data with high precision.
Select and integrate appropriate LLM orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI) based on use-case requirements.

Engineering & Development

Implement tool-use capabilities (function calling), enabling agents to interact with internal APIs, databases, and third-party SaaS platforms safely.
Develop guardrails and steering mechanisms (e.g., NeMo Guardrails, LMQL) to ensure agents stay "on-rails" and avoid hallucinations or unsafe actions.
Optimize prompt engineering strategies (Chain-of-Thought, ReAct, Tree of Thoughts) for maximum reliability and minimum latency.
Oversee the transition from prototype to production, ensuring code is modular, testable, and scalable.

Production Operations (LLMOps)

Implement evaluation frameworks (e.g., Ragas, TruLens, DeepEval) to quantitatively measure agent performance, accuracy, and hallucination rates before deployment.
Design observability dashboards (using tools like LangSmith, Arize, or Datadog) to trace agent reasoning steps, token usage, and latency in real-time.
Manage cost and performance trade-offs, implementing caching strategies and selecting the right model mix (e.g., routing simpler tasks to smaller/cheaper models like GPT-4o-mini or Llama 3).

Technical Qualifications

Core Tech Stack

Languages: Expert proficiency in Python; familiarity with TypeScript is a plus.
LLM Frameworks: Deep experience with LangChain and specifically agentic libraries like LangGraph, AutoGen, or Semantic Kernel.
Vector Databases: Experience deploying and managing vector stores like Pinecone, Weaviate, Qdrant, or pgvector.
Model APIs: Hands-on experience integrating OpenAI (GPT-4), Anthropic (Claude), and open-source models (via Ollama or vLLM).

Infrastructure & DevOps

Experience containerizing AI applications (Docker, Kubernetes) for cloud deployment (AWS/Azure/GCP).
Familiarity with serverless architectures for handling asynchronous agent tasks.
Knowledge of API security standards (OAuth, API Keys) for securing agent tool access.

Nice-to-Haves (The "Edge")

Experience fine-tuning small language models (SLMs) for specific domain tasks to reduce costs and improve latency.
Background in Graph RAG (using Knowledge Graphs alongside Vector DBs) for better reasoning capabilities.
Experience dealing with structured outputs (using Pydantic/Instructor) to force LLMs to return valid JSON/Schematic data.

AI Architect

Job Description