We are representing our client in the marine sector to look an Assistant Manager, AI Engineering. This role is the technical authority for how AI agents and AI-powered applications are built at the company. You will define the platforms,frameworks, standards, and architecture that builders develop with, and act as the embedded AI consultant across the organisation. The scope spans platform strategy, multi agent design, knowledge base architecture, governance partnership, and technical influences.

Responsibilities:

• Act as the embedded AI technical authority on agent design, model selection, and retrieval architecture and integration patterns.
• Author and maintain the company's AI engineering standards, frameworks, and reusable components that builders develop with.
• Design the multi agent architecture, including agent collaboration patterns, the agent mesh, and discovery mechanisms.
• Lead the design and rollout of the enterprise knowledge base, from architecture decisions through to retrieval patterns.
• Define and govern the AI platform estate, including agent development tools, LLM Ops tooling, and LLM provider selection.
• Partner with relevant stakeholders across the organisation on governance, deployment, and operations of AI systems.

Requirements:

• Minimum 8+ years of engineering experience, including significant time in AI, ML, or LLM focused roles.
• Track record of selecting, integrating, and governing AI platforms or LLM Ops tooling at enterprise scale.
• Deep understanding of LLM application architecture, including retrieval augmented generation, vector databases, agent frameworks (LangGraph, LangChain, Semantic Kernel, or equivalent), and prompt engineering.
• Strong working knowledge of model evaluation, observability, and guardrail frameworks (e.g. Pydantic, OpenTelemetry, Langfuse, OPA or similar).
• Demonstrated ability to compare LLM providers and articulate tradeoffs across capability, cost, governance, and lock in.
• Excellent written communication skills. You will be writing standards, decision records, and reference patterns that other engineers rely on.
• Strong ability to influence without authority and earn technical credibility across multiple teams.
• Experience designing multi agent systems and agent orchestration patterns.
• Familiarity with the Model Context Protocol (MCP) and emerging agent interoperability standards.
• Experience across multiple enterprise LLM platforms and ecosystems, with the ability to compare and integrate them.
• Background in building or operating enterprise knowledge bases at scale.
• Exposure to AI governance and Responsible AI frameworks (ISO 27001, NIST AI RMF, model risk management).
• Experience in shipping, logistics, supply chain, or other operational industries preferred.