Senior AI Engineer

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 take direct technical ownership over key parts of our AI ecosystem — 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 autonomy, stripping away micromanagement so you can focus purely on building clean, high-impact architecture.

You'll take ownership of:

• Hands-on Software Engineering: Spend the majority of your time writing production code, implementing robust backend software components, and building reliable asynchronous execution pipelines alongside AI models.
• Agentic Framework Design: 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, context, and orchestration layers. We care about outcomes in production, not which vector database or vendor you used to get there.
• AI-Augmented Development: Model the engineering craft using modern AI tools (Cursor, Claude Code, Copilot) as a core part of your daily workflow to accelerate iteration, architecture design, and system delivery.
  

About you:

• Senior AI / ML Engineering Depth: 7–15 years of overall software engineering experience, including 3+ years in senior AI engineering or ML engineering roles. You have deep software engineering fundamentals and proven hands-on experience designing, writing, and scaling production-grade machine learning and AI systems.
• Engineering Rigor: You value clean, testable code and have strong programming skills in Python alongside asynchronous execution models.
• Production LLM & Agentic Frameworks: You feel at home with modern LLM tooling and orchestration frameworks such as LangGraph and Microsoft Agent Framework (MAF) — and, more importantly, have practical experience taking agentic systems into production, not just prototyping them.
• Agentic Design Patterns: You understand when and how to apply core agentic patterns, including Plan-and-Solve / Plan-and-Execute, ReAct (Reason + Act), and Reflection / Reflexion, and can explain the trade-offs between them in reliability, cost, and latency.
• Context & Prompt Engineering: You have hands-on experience with prompt engineering and context engineering — designing what goes into a model's context window (instructions, retrieved knowledge, memory, tool outputs) so agents behave reliably and stay within budget.
• Retrieval, RAG & Semantic Search: You have practical experience building RAG pipelines and semantic search architectures, including chunking, embeddings, hybrid search, and re-ranking. Experience with vector databases such as PostgreSQL/pgvector or Milvus is valued; specific vendor expertise is not required.
• AI Evaluation & Measurement: You know how to measure AI systems in production — quality, cost, and latency — using evaluation frameworks such as RAGAS and DeepEval, 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 requires.
• 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.

• Autonomous Milestone Execution: You manage your deadlines comfortably, balancing complete end-to-end task ownership with a clear commitment to sharing technical context across the wider organization.
• Linguistic Edge: You speak and write English fluently to collaborate comfortably 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.