Senior AI Forward Deployed Engineer

Technical Competencies (technical Skills Required To Perform The Role) MUST-HAVES

• Senior, hands-on engineer who ships, with recent production AI delivery experience.
• Production LLM and agentic AI: RAG, orchestration and multi-step agent workflows.
• MLOps depth: CI/CD, model registries, monitoring and evaluation pipelines.
• Strong Python and modern software-engineering practice.
• Consulting-adjacent skills to work with non-technical operational stakeholders, identify the real problem and manage delivery end to end.
• Self-driven with minimal direction: scope the work, set the plan and drive it to production without waiting for a brief.
• Technical leadership without formal authority across engineers, analysts and operational staff; ability to set adopted standards and influence senior stakeholders.
• Strong product mindset: user feedback, feature prioritization, technical trade-offs and adoption.
• Deep AI evaluation expertise: groundedness, hallucination detection, task success, latency, safety, business KPIs, A/B testing and continuous regression testing.
• Production AI cost optimization: model selection/routing, prompt and token optimization, caching and inference-cost management.

NICE-TO-HAVES

• Domain exposure to container terminals, maritime logistics, free zones or freight operations, including TOS data, gate and yard processes, customs/trade documentation, or warehouse/contract-logistics workflows.
• GCC or comparable multi-country regional experience; Arabic is a plus.
• Platform engineering or developer-experience background, including internal tooling adopted by other engineers.

Principal Responsibilities

• Embed with operational teams — terminal operations, gate and yard planning, trade documentation, logistics and economic zones — to scope, build and ship AI solutions against live business problems.
• Lead cross-functional delivery squads drawn from the business and IT without formal authority over team members.
• Take LLM and agentic systems from prototype to embedded production, owning security, data governance and operational handover.
• Build accelerators: reusable components, prompt and agent templates, reference architectures and internal libraries that reduce delivery time for subsequent projects.
• Stand up the Lab’s MLOps/AIOps foundation, including CI/CD for models and agents, evaluation pipelines, monitoring, versioning and deployment standards.
• Codify how AI gets built: convert one-off builds into a documented, repeatable delivery methodology the wider organization can run.
• Set and enforce engineering standards, including coding norms, responsible-AI guardrails and documentation, across all Lab delivery.