Senior AI Forward Deployed Engineer
Key Skills for This Role
Full Job Posting
Must-Have Technical Competencies
- Senior hands-on engineer with recent production AI delivery experience.
- Production LLM and agentic AI experience, including RAG, orchestration, and multi-step agent workflows.
- MLOps depth across CI/CD, model registries, monitoring, and evaluation pipelines.
- Strong Python skills and modern software engineering practice.
- Ability to work with non-technical operational stakeholders, identify problems, and manage delivery end to end.
- Ability to scope work, set a plan, and drive it to production with minimal direction.
- Technical leadership without formal authority and ability to influence senior stakeholders.
- Product mindset covering user feedback, feature prioritization, technical trade-offs, and adoption.
- AI evaluation expertise across groundedness, hallucination detection, task success, latency, safety, business KPIs, A/B testing, and regression testing.
- Production AI cost optimization through model selection, routing, prompt and token optimization, caching, and inference-cost management.
Nice-to-Have Experience
- Exposure to container terminals, maritime logistics, free zones, freight operations, TOS data, gate and yard processes, customs documentation, or warehouse workflows.
- GCC or comparable multi-country regional experience.
- Arabic language ability is a plus.
- Platform engineering or developer-experience background, including internal tooling adopted by engineers.
Principal Responsibilities
- Embed with terminal operations, planning, documentation, logistics, and economic-zone teams to scope, build, and ship AI solutions for live business problems.
- Lead cross-functional delivery squads from business and IT without formal authority over members.
- Take LLM and agentic systems from prototype to embedded production, owning security, data governance, and operational handover.
- Build reusable components, prompt and agent templates, reference architectures, and internal libraries.
- Establish the Lab's MLOps and AIOps foundation with CI/CD, evaluation pipelines, monitoring, versioning, and deployment standards.
- Convert one-off builds into a documented, repeatable AI delivery methodology.
- Set and enforce coding norms, responsible-AI guardrails, and documentation standards across Lab delivery.
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