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; specifi