Senior Production GenAI Engineer

Job Description Roles & Responsibilities The AI & Innovation Lab requires a Senior AI Forward Deployed Engineer to embed directly with operational teams across terminal operations, gate and yard planning, trade documentation, logistics and economic zones. The resource will scope, build and deploy production AI solutions against live operational problems and lead cross-functional delivery squads drawn from the business and IT without formal authority. The role will take LLM, RAG and agentic AI solutions from prototype to embedded production, including security review, data-governance alignment and operational handover. The resource will build reusable production accelerators, prompt and agent templates, reference architectures, and internal libraries to reduce delivery time for subsequent AI projects. The position will also establish the Lab’s MLOps/AIOps foundation, including CI/CD for models and agents, model registries, evaluation pipelines, monitoring, versioning and deployment standards. The engineer will define engineering standards, responsible-AI guardrails and repeatable delivery playbooks that can be adopted across the wider organization. This role is required to deliver live AI systems within port, zone and logistics operations; improve AI quality through automated and human evaluation; and optimize production AI performance, latency and operating cost. The requirement supports DP World’s direction toward reusable, scalable and value-driven AI capabilities, with regional delivery aligned to enterprise governance and standards. Responsibilities: Operate through a forward-deployed model by working directly with operational teams where the business problem exists. 2. Cover live use cases across terminal operations, gate and yard planning, trade documentation, logistics and economic zones. Translate ambiguous operational requirements into an AI product scope, delivery plan and production solution. Build and deploy LLM-based and agentic systems, including RAG, orchestration and multi-step agent workflows. Own the full production path: security, data governance, testing, deployment, operational handover and ongoing improvement. Lead mixed teams of engineers, analysts and operational staff without formal authority and carry the technical argument with senior stakeholders. Build concrete reusable assets rather than presentations: production accelerators, prompt/agent templates, reference architectures and internal libraries. Establish the MLOps/AIOps platform foundation for deploying, evaluating, monitoring and versioning models and agents. Create delivery playbooks and a documented, repeatable methodology that the wider organization can run. Measure real-world AI quality through automated and human evaluation: groundedness, hallucination detection, task success, latency, safety and business KPIs. Apply A/B testing and continuous regression testing so production systems improve over time. Optimize production AI cost through model selection/routing, prompt and token-usage optimization, caching and inference-cost management while balancing performance and latency. 13. Deliver live AI systems operating inside port, zone and logistics operations against real cargo, gates and trade flows. Work directly with business-unit leaders; the Lab reports into senior leadership and has a mandate to move quickly from validated idea to production decision. Desired Candidate Profile 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. GCC Experience & Arabic capability 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. Employment Type Full-time Company Industry IT - Software Services Department / Functional Area IT Software Keywords Agentic AIRAGLLMPythonMLOpsAI EvaluationProduction Get real-time job updates only on our App