AI/ML Engineer

Brio Digital; Remote; GBP 60,000 - 90,000; Full time

Job summary

A leading technology company is seeking a Senior Machine Learning Engineer to design and produce applications in Generative AI and Large Language Models (LLMs). The successful candidate will lead the development of scalable systems using GCP and Google’s AI ecosystem, working fully remotely within a collaborative team. Key responsibilities include developing ML models, architecting pipelines, and ensuring production quality. Applicants should have extensive experience in machine learning and strong Python skills, particularly with LLMs and Generative AI.

Benefits

High autonomy and ownership

Competitive salary

Opportunity to influence architecture

Qualifications

• 5+ years' experience in machine learning engineering or applied AI roles.
• Recent experience with LLMs, Generative AI, and/or RAG-based systems.
• Solid understanding of deep learning and statistical modelling.

Responsibilities

• Design, develop, and deploy advanced machine learning models.
• Architect LLMOps pipelines on GCP, including fine-tuning and vector search.
• Monitor and improve models in production environments.

Skills

Machine Learning Engineering

Python (PyTorch, TensorFlow)

GCP Vertex AI

APIs

Docker

CI/CD

Git

Statistical Modelling

Optimisation Techniques

Vector Databases

Tools

Hugging Face

Google GenAI

Job description

AI/ML Engineer (Generative AI / LLMs)

Location: Fully Remote (UK-based)

The Role

We’re hiring a Senior Machine Learning Engineer to lead the design and production 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

Essential

• 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

• Work on real, production-grade GenAI systems with clear business impact.
• High autonomy and ownership in a senior, hands‑on engineering role.
• Fully remote working with a collaborative, distributed team.
• Opportunity to influence architecture and long‑term technical direction.
• Competitive salary