AI Architect (TL)

About Us UMO is a stealth-mode FinTech venture aiming to evolve the way people experience money by building a unified, AI-powered, yet deeply human modern money platform across fiat, crypto, and investments - subject to regulatory approvals. The platform is being designed to break down traditional barriers to money across access, assets, and experience, enabling simpler, more adaptive ways for people to interact with financial services. We are currently developing our MVP and navigating licensing requirements, with a multidisciplinary team of 100+ people representing 20+ nationalities. With our headquarters in the UAE and offices in Portugal and Ukraine, we are united behind a shared ambition and a relentless focus on serving our customers. About this role As a part of our team, you'll help us shape a platform that works for One — a business owner, an artist, a freelancer. In this role, you will lead the core technical design of our AI ecosystem and run, grow, and mentor the AI engineering squad — ensuring intelligence feels invisible, thoughtful, and deeply integrated into our financial workflows rather than tacked on as an afterthought. We provide the space and trust for you to operate with true technical and people-leadership autonomy, stripping away micromanagement so you can focus on building clean, high-impact architecture and a strong team that makes the old way of banking feel entirely outdated. You'll take ownership of: • Squad Leadership & Mentorship: Lead, grow, and mentor a high-performing AI engineering squad, running code reviews, sharing architectural context, and making mentorship an expected, daily practice. • As Team Lead, you: • Own outcomes end-to-end, including dependencies on other teams — success is features live in production, not tasks closed. • Run and grow the team,mentor the AI team. • Act as the reference point — the “lighthouse” — in your specialty for other teams. • Stay hands-on: you ship code every week. • Core AI Architecture: Design and build the fundamental technical blueprints for our unified intelligence layer, integrating large language models and retrieval systems smoothly with our transactional infrastructure. • Agentic Framework Design: Lead the development of deterministic-agentic bridges, ensuring complex autonomous tasks operate within predictable, secure, and compliant financial boundaries. • Production Deployment & Scale: Take full responsibility for bringing machine learning models out of research prototypes and into stable, highly performant production code. • Agentic Systems in Production: Collaborate closely with backend and data engineers to take agentic workflows from prototype to reliable, monitored production systems — with practical experience across retrieval, memory, and orchestration layers as needed. We care about outcomes in production, not which vector database or vendor you used to get there. About you: • Engineering Leadership & Depth: You're a senior engineer (5+ yrs) with real experience leading a team — as a Tech Lead, Team Lead, or equivalent — combined with a deep architectural background in production-grade machine learning and AI systems. Adjacent leadership experience is fine; the leadership itself has to be real. • Production LLM & Agentic Frameworks: You feel at home with modern LLM tooling and orchestration frameworks, and — more importantly — have practical experience taking agentic systems into production, not just prototyping them. • Retrieval & Semantic Search: You have practical experience with semantic search and retrieval architectures. Specific vector database expertise is not required. • AI Evaluation & Measurement: You know how to measure AI systems in production — quality, cost, and latency — and can prove the value of what you build, not just ship it. • PII & Data Privacy: You have experience handling personally identifiable information and sensitive data with LLMs in a regulated environment, and understand the guardrails that require. • Regulated-Environment Comfort: You're comfortable designing and shipping AI systems within strict security, compliance, and audit boundaries. That's the must-have; fintech- or crypto-specific background is not (see bonus points). • Engineering Rigor: You value clean, testable code and possess strong programming skills in Python alongside asynchronous execution models. • AI-Augmented Development: You use AI coding tools (Cursor, Claude Code, Copilot, or similar) as a core part of your daily workflow — expected practice in an AI-native org, especially for the person architecting our AI systems. • Autonomy & Deadlines: You manage your deadlines comfortably, balancing individual task ownership with a clear commitment to sharing technical context across the wider organization. • Linguistic Edge: You speak and write English fluently to collaborate with our multidisciplinary teams spread across different parts of the world. Can get you a few bonus points: • Startup Exposure: You have experience working in startups and know how to keep your technical designs flexible and adaptive as product targets evolve. • Fintech or Crypto Literacy: You've previously built software inside regulated financial or web3 spaces. Nice to have, not required — we know the AI talent pool with fintech experience is small. • Agentic Deployments: You have a proven track record of shipping live, multi-agent automated workflows to production. The UMO Standard • Dynamic Work Environment: Vibrant offices and strong local teams across Lisbon, Dubai, Kyiv, and Lviv — with openness to remote for the right fit. • Rest & Recovery: 24 days of annual leave, dedicated paid sick leave, and observance of Public Holidays to keep you at your best. • Recharge Week: After your first year with us, enjoy two consecutive 4-day work weeks annually — a built-in reset to help you sustain peak performance for the long run. • Full Setup: Top-of-the-line hardware and a home office stipend so you can do your best work anywhere. • Growth Ecosystem: A dedicated learning budget and a clear, accelerated path toward executive-level leadership within the UMO ecosystem. • Impact & Ownership: Your work directly shapes the product and company direction — not just executes it. Show more

Engineering Leadership & Depth: You're a senior engineer (5+ yrs) with real experience leading a team — as a Tech Lead, Team Lead, or equivalent — combined with a deep architectural background in production-grade machine learning and AI systems. Adjacent leadership experience is fine; the leadership itself has to be real. Production LLM & Agentic Frameworks: You feel at home with modern LLM tooling and orchestration frameworks, and — more importantly — have practical experience taking agentic systems into production, not just prototyping them. Retrieval & Semantic Search: You have practical experience with semantic search and retrieval architectures. Specific vector database expertise is not required. AI Evaluation & Measurement: You know how to measure AI systems in production — quality, cost, and latency — and can prove the value of what you build, not just ship it. PII & Data Privacy: You have experience handling personally identifiable information and sensitive data with LLMs in a regulated environment, and understand the guardrails that require. Regulated-Environment Comfort: You're comfortable designing and shipping AI systems within strict security, compliance, and audit boundaries. Engineering Rigor: You value clean, testable code and possess strong programming skills in Python alongside asynchronous execution models.

Lead, grow, and mentor a high-performing AI engineering squad, running code reviews, sharing architectural context, and making mentorship an expected, daily practice. Own outcomes end-to-end, including dependencies on other teams — success is features live in production, not tasks closed. Run and grow the team, mentor the AI team. Act as the reference point — the “lighthouse” — in your specialty for other teams. Stay hands-on: you ship code every week. Design and build the fundamental technical blueprints for our unified intelligence layer, integrating large language models and retrieval systems smoothly with our transactional infrastructure. Lead the development of deterministic-agentic bridges, ensuring complex autonomous tasks operate within predictable, secure, and compliant financial boundaries. Take full responsibility for bringing machine learning models out of research prototypes and into stable, highly performant production code. Collaborate closely with backend and data engineers to take agentic workflows from prototype to reliable, monitored production systems — with practical experience across retrieval, memory, and orchestration layers as needed.