Senior AI Forward Deployed Engineer

Role Overview

Technical Competencies

• Recent production AI delivery experience is required.
• Production LLM and agentic AI experience must include retrieval-augmented generation, orchestration, and multi-step agent workflows.
• MLOps depth must include CI/CD, model registries, monitoring, and evaluation pipelines.
• Strong Python and modern software-engineering practice are required.
• Consulting-adjacent skills are needed to work with non-technical operational stakeholders and manage delivery end to end.
• The engineer must be self-driven, able to scope work, set plans, and drive delivery to production with minimal direction.
• Technical leadership without formal authority is required across engineers, analysts, and operational staff.
• A strong product mindset is required, including user feedback, prioritization, trade-offs, and adoption.
• Deep AI evaluation expertise must cover groundedness, hallucination detection, task success, latency, safety, business KPIs, A/B testing, and regression testing.
• Production AI cost optimization must cover model selection and routing, prompt and token optimization, caching, and inference-cost management.

Nice-to-Have Experience

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

Principal Responsibilities

• Embed with terminal operations, gate and yard planning, trade documentation, logistics, and economic-zone teams to scope, build, and ship AI solutions against live business problems.
• Lead cross-functional delivery squads drawn from 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 reusable components, prompt and agent templates, reference architectures, and internal libraries to reduce delivery time.
• Stand up the Lab's MLOps and AIOps foundation, including CI/CD, evaluation pipelines, monitoring, versioning, and deployment standards.
• Convert one-off builds into a documented, repeatable delivery methodology for the wider organization.
• Set and enforce coding norms, responsible-AI guardrails, and documentation standards across Lab delivery.

Named Skills

• Python, MLOps, and LLM are specifically identified as skills for the role.