AI/ML Engineer

Brio Digital

United Kingdom

On-site

GBP 90,000 - 110,000

Full time

14 days+

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Benefits offered by this job

High autonomy

Opportunity to influence architecture

Collaborative team environment

Job summary

A leading technology firm is hiring a Senior Machine Learning Engineer to design and productionise Generative AI applications. This fully remote role involves architecting LLMOps pipelines, developing machine learning models, and collaborating with cross-functional teams. Candidates should have 5+ years of relevant experience, strong Python skills, and expertise in GCP. The position offers a competitive salary up to £100k with significant ownership in a collaborative environment.

Qualifications

5+ years’ experience in machine learning engineering or applied AI roles.
Strong Python skills using frameworks such as PyTorch, TensorFlow, Hugging Face, or Google GenAI.
Proven experience designing and operating large-scale ML systems in production.

Responsibilities

Design, develop, and deploy advanced machine learning and deep learning models into production.
Architect scalable LLMOps pipelines on GCP / Vertex AI.
Collaborate with product and engineering teams to translate complex business requirements into ML-driven solutions.

Skills

Machine learning engineering

Generative AI

LLMs

Python

GCP

Docker

Tools

PyTorch

TensorFlow

Hugging Face

Google GenAI

vector databases

Job description

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Senior Delivery Consultant at Brio Digital sourcing and placing across Brio Partners and Brio Logic.

Location: Fully Remote (UK-based)

The Role

We’re hiring a Senior Machine Learning Engineer to lead the design and productionisation of Generative AI and Large Language Model (LLM) applications. This role sits at the heart of an AI-focused engineering team, delivering scalable, production-grade systems using GCP and Google’s AI ecosystem.

You’ll be a senior, hands-on engineer owning complex technical problems end to end, with a strong influence over architecture, tooling, and the future direction of LLM-powered products.

What You’ll Be Doing

Design, develop, and deploy advanced machine learning and deep learning models into production.
Architect scalable LLMOps pipelines on GCP / Vertex AI, including fine-tuning, vector search, and low-latency inference.
Build end-to-end LLM applications, leveraging RAG (Retrieval-Augmented Generation), agentic workflows, and prompt engineering.
Implement robust evaluation frameworks to monitor LLM quality, hallucinations, token usage, and content safety.
Develop and deploy autonomous or semi-autonomous agents using modern agent frameworks and Google AI tooling.
Collaborate with product and engineering teams to translate complex business requirements into ML-driven solutions.
Monitor, optimise, and continuously improve models in live production environments.
Contribute to the architecture and evolution of the AI platform and supporting data infrastructure.
Stay current with emerging research, tools, and best practices across ML and Generative AI.

What We’re Looking For

5+ years’ experience in machine learning engineering or applied AI roles.
Recent, demonstrable experience with LLMs, Generative AI, and/or RAG-based systems.
Strong Python skills using frameworks such as PyTorch, TensorFlow, Hugging Face, or Google GenAI.
Experience with vector databases and retrieval-based architectures.
Proven experience designing and operating large-scale ML systems in production.
Strong experience with GCP Vertex AI (or equivalent cloud ML platforms).
Solid software engineering fundamentals: APIs, Docker, CI/CD, and Git.
Strong understanding of deep learning, statistical modelling, and optimisation techniques.

Nice to Have

Experience with agentic design patterns (e.g. ReAct, Chain-of-Thought, tool use).
Familiarity with LLM evaluation frameworks such as RAGAS or TruLens.
Experience fine-tuning large models or working with reinforcement learning techniques.
Background in mathematics, statistics, or theoretical computer science.
Understanding of data governance, bias mitigation, or model interpretability.

Why Join