AI Architect – GenAI & Cloud f m x

Job description

Most AI initiatives stall somewhere between a promising prototype and a system that actually runs in production. We need an architect who closes that gap — someone who can look at a client’s problem, design a solution that makes technical sense, estimate what it will take to build, and then stay involved long enough to make sure it ships. You’ll work across both generative and classical AI pipelines — knowledge-grounded AI systems, agent frameworks, model serving, and data infrastructure on the GenAI side, alongside prediction, recommendation, and optimization models where they’re the right tool for the job. You’ll also be client-facing: leading technical conversations, pulling apart RFPs, scoping projects, and being honest about what’s feasible and what isn’t. This role requires deep fluency in at least one major cloud platform (Azure, AWS, or GCP) and the judgment to know when a simpler solution is the better one. You will report directly to the Head of AI. Expect a roughly 50/50 split between hands-on technical leadership on active projects and preparing new engagements — scoping, estimating, and shaping proposals. What we offer: AI Grant — Stop talking about AI and start building it. Our AI Grant gives you dedicated budget and resources to turn your wildest AI idea into a working project, backed by two paid weeks to focus on nothing else. AI Center of Excellence — Work alongside specialists in agentic AI, sovereign AI, generative and discriminative AI. This isn’t a siloed team — it’s the people you’ll learn from and build with daily. Your tools, your choice — Full access to AI-powered development tools including Claude, Cursor, and GitHub Copilot. Pick what works best for you. Real project variety — From generative AI for legal document compliance, through agentic systems in manufacturing environments, to enterprise-scale AI platforms, computer vision, and autonomous driving. You won’t get bored. Conference and speaking support — Want to attend conferences? We’ll back you. Want to speak at them? Even better — we’ll support you with dedicated preparation time and bonuses. Your tasks Lead client-facing engagements — discovery sessions, technical workshops, architecture presentations — and translate business problems into well-defined AI solution specifications Analyze RFP/RFI documents, assess feasibility, and build technical proposals including architecture diagrams, delivery timelines, team composition, and cost breakdowns Scope and estimate AI projects end-to-end — from data readiness and infrastructure needs through cloud costs and ongoing operational expenses Design end-to-end architectures for generative and classical AI systems: knowledge-grounded AI, agentic workflows, multi-model orchestration, and hybrid search solutions Make and defend technology choices — foundation models, vector databases, orchestration frameworks, inference infrastructure — matching each to the problem, not the 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 At least 10 years in software/data/ML engineering, with at least 3 years focused on AI/ML architecture at scale Deep, production-level fluency in at least one major cloud AI ecosystem: Azure, AWS, or GCP Experience in client-facing roles — comfortable leading technical workshops and presenting to both C-level and engineering audiences Ability to scope and budget AI projects, including cloud cost modeling, team sizing, and delivery planning Strong hands-on skills in Python and practical experience with AI/ML frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent Practical experience building knowledge-grounded AI systems and agent architectures in production — not just prototypes Solid understanding of transformer architectures and how they affect system design decisions (context windows, latency, cost) Fluent English, both written and spoken Fluent Polish required Residing in Poland required Nice-to-have requirements Experience fine-tuning LLMs (LoRA, QLoRA, RLHF/DPO) and understanding when fine-tuning is worth the investment vs. in-context learning Background in consulting, system integration, or services delivery with exposure to enterprise procurement processes Contributions to open-source AI/ML projects or published technical writing Experience with graph databases for knowledge graph-augmented search and reasoning Background in classical ML — because not every problem needs an LLM

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english consulting ci/cd polish generative ai python aws prompt engineering software engineering cloud gcp azure claude vector databases genai data engineering technical writing cost system integration langgraph client-facing agentic ai rfps computer vision mlops dpo langchain ai engineering cursor orchestration frameworks foundation models data infrastructure ai-powered development tools autonomous driving feasibility semantic kernel agent frameworks github copilot technical proposals llmops latency delivery planning delivery timelines graph databases agentic workflows cloud costs model serving project scoping discovery sessions lora llm fine-tuning services delivery transformer architectures recommendation models operational expenses cost breakdowns engineering audiences optimization models qlora knowledge graph head of ai rlhf chunking strategies data readiness ai pipelines architecture decision records technical workshops classical ml sovereign ai ai center of excellence ai architect architecture diagrams team composition evaluation pipelines ml engineering context windows enterprise procurement inference infrastructure embedding generation prediction models in-context learning technical design reviews technical conversations classical ai knowledge-grounded ai ai grant discriminative ai conference support speaking support architecture presentations solution specifications rfi documents infrastructure needs end-to-end architectures multi-model orchestration hybrid search solutions drift monitoring ai/ml architecture cloud ai ecosystem c-level presentations cloud cost modeling team sizing open-source ai/ml

AI Architect – GenAI & Cloud f m x

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