About the role

We are looking for an experienced Assistant Manager, AI Engineering for one of our global logistics clients. The person will serve as the technical authority for the development of AI agents and AI-powered applications across the company. This role is responsible for defining the platforms, architectures, frameworks and engineering standards that enable teams to build reliable, scalable and governed AI solutions. You will act as an embedded AI consultant to the organisation, advising on agent design, platform strategy, knowledge base architecture, model selection and integration patterns. The role requires strong technical depth, architectural judgement and the ability to influence engineering practices across teams.

Key responsibilities

Serve as the organisation's embedded AI technical authority for agent design, model selection, retrieval architecture and integration patterns.

Develop and maintain AI engineering standards, technical frameworks, reference architectures and reusable components for internal builders.

Design the company's multi-agent architecture, including agent collaboration patterns, agent mesh concepts and discovery mechanisms.

Lead the architecture, design and rollout of the enterprise knowledge base, including retrieval strategies and implementation patterns.

Define and govern the AI platform landscape, including agent development tools, LLMOps tooling and LLM provider selection.

Partner with stakeholders across the organisation on the governance, deployment and operational management of AI systems.

Provide technical guidance to teams building AI-powered applications and ensure solutions align with company standards and governance requirements.

About you

At least eight years of engineering experience, including substantial experience in AI, machine learning or LLM-focused roles.

Proven experience selecting, integrating and governing AI platforms or LLMOps tooling at enterprise scale.

Strong understanding of LLM application architecture, including retrieval-augmented generation, vector databases, agent frameworks and prompt engineering.

Experience with agent frameworks such as LangGraph, LangChain, Semantic Kernel or equivalent technologies.

Strong working knowledge of model evaluation, observability and guardrail frameworks, such as Pydantic, OpenTelemetry, Langfuse, OPA or similar tools.

Ability to assess and compare LLM providers, clearly articulating trade-offs across capability, cost, governance, security and vendor lock-in.

Excellent written communication skills, with the ability to produce technical standards, decision records and reference patterns used by engineering teams.

Strong ability to influence without direct authority and build technical credibility across multiple teams.

Experience designing multi-agent systems and agent orchestration patterns.

Familiarity with the Model Context Protocol and emerging standards for agent interoperability.