AI Engineer

IOTA's Data & AI practice builds systems that run inside the customer's perimeter. Our clients are banks, financial institutions and public-sector bodies across the GCC, where data residency, access control and auditability are the starting conditions rather than late additions. As an AI Engineer you will build those systems end to end.

What you'll do

• Build retrieval-augmented generation systems over enterprise data, with retrieval that inherits existing access rights rather than flattening them
• Deploy and serve open-weight models in on-premises, hybrid and air-gapped environments
• Build evaluation harnesses and regression suites so model behaviour is measurable before it reaches production
• Implement guardrails, observability and human-in-the-loop review for systems that must answer to a regulator
• Work with our Infrastructure practice on GPU capacity, scheduling and model lifecycle
• Contribute to IOTA Labs products — Qantara, iChat, iCompliance and iInvestmentHouse

What we offer

• Competitive salary, reviewed annually
• Fully remote, with flexible hours across GCC time zones
• Production work in regulated environments — systems that ship, not proofs of concept
• Structured mentoring, and a defined path from trainee to engineer for those joining early in their career

What we're looking for

• Strong Python, and comfort reading and writing production code
• Practical experience with modern LLM tooling — embeddings, vector search, retrieval pipelines, prompt and context design
• Familiarity with a deep-learning framework such as PyTorch, and with serving models rather than only calling hosted APIs
• An instinct for evaluation — knowing whether a change actually made the system better
• Clear written English; Arabic is an advantage

Trainees and early-career applicants

We accept trainees. If you do not yet have commercial experience, apply anyway — show us something you have built and can explain. Personal projects, open-source contributions, published notebooks and academic work all count. Trainees join with structured mentoring and a defined route to a full engineering role.

Nice to have

• Experience deploying into restricted or air-gapped networks
• Exposure to Kubernetes, containers or GPU scheduling
• Awareness of SAMA, NDMO or PDPL obligations, or the appetite to learn them