Senior AI Forward Deployed Engineer

منذ 4 أيام

Dubai, Dubai Emirate, الإمارات العربية المتحدة Dicetek LLC دوام كامل ‏360,000 € - ‏600,000 €/سنة
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‑Have
- 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.