Senior AI Engineer

UMO

. Take direct technical ownership over key parts of the AI ecosystem .

Posted 10/27/2026full-timeDubai • United Arab EmiratesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying production-grade AI systems, with a strong focus on machine learning, asynchronous execution, and compliance in financial applications. Proven ability to collaborate across teams and manage complex workflows while ensuring security and performance.

Highest-signal resume keywords

Python ProgrammingMachine Learning EngineeringAsynchronous Execution ModelsPrompt EngineeringAgentic Systems Production

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software Engineering FundamentalsProduction Code DevelopmentDeterministic-Agentic BridgesRAG PipelinesSemantic Search ArchitecturesVector DatabasesQuality Measurement of AI SystemsPlan-and-Solve PatternsReAct PatternsContext Engineering

Soft Skills

Technical AutonomyDeadline ManagementCross-Organizational Communication

Tools & Technologies

CursorClaude CodeCopilotLangGraphMicrosoft Agent FrameworkPostgreSQL/pgvectorMilvusRAGASDeepEval

Industry Keywords

AI EcosystemFinancial TechnologyComplianceSensitive Data HandlingStartup Experience

Tech Stack

Tools & technologies

PostgresPythonReact

About the role

Key responsibilities & impact

Take direct technical ownership over key parts of the AI ecosystem
Write production code and implement robust backend software components
Build reliable asynchronous execution pipelines alongside AI models
Develop deterministic-agentic bridges for predictable, secure, and compliant autonomous financial tasks
Bring machine learning models from research prototypes into stable, highly performant production code
Collaborate with backend and data engineers to deploy reliable, monitored agentic workflows
Work across retrieval, memory, context, and orchestration layers
Use Cursor, Claude Code, and Copilot to accelerate iteration, architecture design, and system delivery
Shape an AI-powered financial platform spanning fiat, crypto, and investments
Operate with technical autonomy and influence product and company direction

Requirements

What you’ll need

7–15 years of overall software engineering experience
3+ years in senior AI engineering or ML engineering roles
Deep software engineering fundamentals and hands-on experience designing, writing, and scaling production-grade machine learning and AI systems
Strong programming skills in Python alongside asynchronous execution models
Experience with modern LLM tooling and orchestration frameworks such as LangGraph and Microsoft Agent Framework (MAF)
Practical experience taking agentic systems into production
Understanding of Plan-and-Solve / Plan-and-Execute, ReAct (Reason + Act), and Reflection / Reflexion agentic patterns
Hands-on experience with prompt engineering and context engineering
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
Experience measuring AI systems in production for quality, cost, and latency
Familiarity with evaluation frameworks such as RAGAS and DeepEval
Experience handling personally identifiable information and sensitive data with LLMs in a regulated environment
Comfort designing and shipping AI systems within strict security, compliance, and audit boundaries
Ability to manage deadlines and own end-to-end tasks while sharing technical context across the organization
Fluent spoken and written English
Startup experience is a bonus
Fintech or crypto literacy is nice to have, not required
Proven track record of shipping live, multi-agent automated workflows to production is a bonus

Benefits

Comp & perks

24 days of annual leave
Dedicated paid sick leave
Observance of Public Holidays
Two consecutive 4-day work weeks annually after the first year
Top-of-the-line hardware
Home office stipend
Dedicated learning budget
Clear, accelerated path toward executive-level leadership
Openness to remote for the right fit