Senior Production GenAI Engineer
Role Overview
The AI and Innovation Lab requires a senior AI forward-deployed engineer to work directly with operational teams across terminal operations, gate and yard planning, trade documentation, logistics, and economic zones.
The role scopes, builds, and deploys production AI solutions against live operational problems while leading cross-functional business and IT delivery squads without formal authority.
Key Skills for This Role
Full Job Posting
Role Overview
The AI and Innovation Lab requires a senior AI forward-deployed engineer to work directly with operational teams across terminal operations, gate and yard planning, trade documentation, logistics, and economic zones.
The role scopes, builds, and deploys production AI solutions against live operational problems while leading cross-functional business and IT delivery squads without formal authority.
Core Responsibilities
- Take LLM, RAG, and agentic AI solutions from prototype to embedded production, including security review, data-governance alignment, and operational handover.
- Build reusable production accelerators, prompt and agent templates, reference architectures, and internal libraries for subsequent AI projects.
- Establish the MLOps/AIOps foundation, including CI/CD for models and agents, model registries, evaluation pipelines, monitoring, versioning, and deployment standards.
- Define engineering standards, responsible-AI guardrails, and repeatable delivery playbooks for wider organizational adoption.
- Deliver live AI systems within port, zone, and logistics operations and improve quality through automated and human evaluation.
- Optimize production AI performance, latency, and operating cost.
Requirements
- Experience delivering production AI systems within operational environments is required.
- Ability to scope, build, and deploy LLM, RAG, and agentic AI solutions from prototype through production is required.
- Ability to align AI solutions with security review and data-governance requirements is required.
- Ability to establish MLOps/AIOps foundations, including CI/CD, registries, evaluation, monitoring, versioning, and deployment standards, is required.
- Ability to define engineering standards, responsible-AI guardrails, and repeatable delivery playbooks is required.
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