AI Architect (GenAI & Cloud)

Quick Facts

• Hands-on technical leadership across active AI projects and preparation of new engagements
• Client-facing work: discovery sessions, technical workshops, architecture presentations
• Scope: generative AI/agentic workflows and classical ML (when appropriate)

Description

Architect end-to-end generative and classical AI solutions from client discovery through production delivery. Analyze RFP/RFI documents, design feasible architectures, estimate delivery effort and costs, and define LLM/MLOps and data pipelines to ensure solutions ship.

Responsibilities

• Lead client engagements (discovery sessions, technical workshops, architecture presentations) and translate business problems into AI solution specifications
• Analyze RFP/RFI documents and build technical proposals including architecture diagrams, delivery timelines, team composition, and cost breakdowns
• Scope and estimate AI projects end-to-end: data readiness, infrastructure needs, cloud costs, and ongoing operational expenses
• Design end-to-end architectures for generative and classical AI: knowledge-grounded AI, agentic workflows, multi-model orchestration, and hybrid search
• Make and defend technology choices (foundation models, vector databases, orchestration frameworks, inference infrastructure) based on the problem—not hype
• Define MLOps/LLMOps practices and data pipelines: CI/CD for models and prompts, evaluation pipelines, chunking strategies, embedding generation, and drift monitoring
• Lead technical design reviews, produce architecture decision records, and mentor engineers on AI engineering best practices

Requirements

• 10+ years in software/data/ML engineering, including 3+ years focused on AI/ML architecture at scale
• Deep production-level fluency in Azure, AWS, or GCP
• Client-facing experience leading technical workshops and presenting to C-level and engineering audiences
• Ability to scope and budget AI projects (cloud cost modeling, team sizing, delivery planning)
• Strong hands-on Python and practical experience with LangChain, LangGraph, Semantic Kernel, or equivalent frameworks
• Production experience building knowledge-grounded AI systems and agent architectures
• Solid understanding of transformer architectures and their impact on context windows, latency, and cost
• Fluent English (written and spoken) and fluent Polish
• Residing in Poland

Benefits

• AI Grant: dedicated budget and resources to turn an AI idea into a working project, plus two paid weeks for focused building
• AI Center of Excellence: collaboration with specialists in agentic AI, sovereign AI, and generative/discriminative AI
• Choice of tools: full access to AI-powered development tools (Claude, Cursor, GitHub Copilot)
• Project variety: examples include legal document compliance, agentic manufacturing, enterprise AI platforms, computer vision, and autonomous driving
• Conference & speaking support: backing for attendance and support for speaking, including preparation time and bonuses