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.
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) * Tax residence in Ukraine is 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 * Previous work with graph databases for knowledge graph-augmented search and reasoning * Background in classical ML — because not every problem needs an LLM
