About The Role

About our Client

Our client is a technology-led organisation operating large-scale software platforms and complex production environments. The company is continuing to strengthen the internal systems and tools that allow engineering teams to deploy, manage, and scale services efficiently.

About the Role

We are looking for a Technical Product Manager to own and evolve the ML and LLM Ops platforms used across the organisation. You will shape how teams build, evaluate, deploy, monitor, and scale machine learning models and LLM-powered applications, working at the intersection of platform engineering, applied AI teams, and the emerging generation of AI coding assistants and agents.

This is a deeply technical product role. The goal is not only to support ML and LLM applications in production, but to reduce the friction of building them, so that development becomes faster, safer, and increasingly automated. It suits someone from an ML, data, or platform engineering background who has moved into product and enjoys owning technical infrastructure used by engineers.

Responsibilities

Own the product vision and roadmap for the ML and LLM Ops platforms, from concept and specification through to launch and post-launch analysis.
Shape how teams productionise ML models and LLM applications, covering the full model lifecycle: training, evaluation, versioning, deployment, monitoring, retraining, and observability.
Gather requirements from ML engineers, data scientists, backend engineers, and AI application teams, and translate them into clear priorities and technical product plans.
Identify platform gaps, scalability constraints, and optimisation opportunities, and turn them into roadmap items.
Balance experimentation flexibility against governance, safety, reliability, and cost efficiency.
Partner with engineering on architecture, API design, infrastructure trade-offs, and technical debt.
Drive the evolution of the platform towards AI-native development, so that tooling, APIs, and abstractions support meaningful contributions from AI coding assistants and autonomous agents, with the right guardrails in place.
Define and track metrics covering deployment velocity, platform adoption, model performance, reliability, and cost.
Drive adoption through documentation, enablement, and internal advocacy.

Qualifications

5+ years of experience in ML engineering, data science, platform engineering, or a comparable technical domain.
2+ years of technical product or programme management experience in a high-scale, fast-moving environment.
Hands-on exposure to ML Ops or LLM Ops concepts, including model lifecycle, evaluation, monitoring, versioning, and deployment pipelines.
Familiarity with common LLM application patterns such as RAG, fine-tuning, prompt management, and evaluation frameworks.
Comfortable discussing system design, APIs, infrastructure trade-offs, and scalability directly with senior engineers.
Strong analytical mindset, with the ability to reason about performance and cost trade-offs.
Able to influence without authority across engineering, data science, and business stakeholders.
Comfortable operating in ambiguity and in a rapidly changing technology landscape.

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