AI Solution Architect – GenAI / Agentic AI

We are looking for an experienced AI Solution Architect with a strong software engineering background and hands-on expertise in Python, Generative AI, LLMs, RAG, and Agentic AI orchestration.

The ideal candidate will have 10+ years of experience designing scalable, enterprise-grade systems, with a strong understanding of Domain-Driven Design (DDD), Event-Driven Architecture (EDA), and modern AI architectures.

Candidates who have designed and delivered AI-powered solutions in production, rather than architects whose experience is limited to strategy, presentations, or proof-of-concept projects.

Location: Remote From Poland

Language: Polish and English

Contract type: B2B only

Key Responsibilities

• Design and architect scalable, enterprise-grade GenAI and Agentic AI solutions.
• Define solution architecture using DDD, EDA, microservices, and distributed systems.
• Design and implement AI agents, multi-agent workflows, and orchestration frameworks.
• Architect LLM-based applications, RAG pipelines, and intelligent automation solutions.
• Provide hands-on technical leadership in Python development.
• Integrate AI components with existing enterprise applications, APIs, and data platforms.
• Ensure solutions meet requirements for scalability, security, reliability, performance, and maintainability.
• Lead technical discussions, architectural decisions, code reviews, and engineering best practices.
• Guide development teams through the implementation and production deployment of AI solutions.

Required Skills & Experience

• 10+ years of experience in software engineering, solution architecture, or technical leadership.
• Strong architectural expertise in Domain-Driven Design (DDD) and Event-Driven Architecture (EDA).
• Advanced Python development skills, including backend services and API development.
• Hands-on experience with Large Language Models (LLMs) and enterprise GenAI applications.
• Strong experience designing and implementing Retrieval-Augmented Generation (RAG) solutions.
• Practical experience with LangGraph (LG) and LangChain (LCH).
• Proven expertise in Agentic AI orchestration, including autonomous agents, multi-agent systems, tool calling, and workflow coordination.
• Strong knowledge of microservices, REST APIs, distributed systems, and integration patterns.
• Experience designing AI solutions for production environments.