Description

Job Description

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: 

1. 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. 

2. Lead cross-functional delivery squads drawn from the business and IT without formal authority over team members. 

3. Take LLM and agentic systems from prototype to embedded production, owning security, data governance and operational handover. 

4. Build accelerators: reusable components, prompt and agent templates, reference architectures and internal libraries that reduce delivery time for subsequent projects. 

5. Stand up the Lab’s MLOps/AIOps foundation, including CI/CD for models and agents, evaluation pipelines, monitoring, versioning and deployment standards. 

6. Codify how AI gets built: convert one-off builds into a documented, repeatable delivery methodology the wider organization can run. 

7. Set and enforce engineering standards, including coding norms, responsible-AI guardrails and documentation, across all Lab delivery

Key Skills

Education

Any Graduate