AI/ML Engineer

Brio Digital; Remote; GBP 70,000 - 90,000; Full time

Job summary

A technology company specializing in AI solutions is seeking a Senior Machine Learning Engineer to lead the design and production of Generative AI applications. This remote position focuses on developing scalable ML systems and requires over 5 years of experience in machine learning. Candidates should have strong Python skills and be familiar with tools such as GCP Vertex AI. The position offers high autonomy and the chance to influence technical direction while working on real-world AI projects.

Benefits

Competitive salary

Work on production-grade systems

High autonomy in engineering role

Qualifications

• 5+ years' experience in machine learning engineering or applied AI roles.
• Recent experience with LLMs, Generative AI, or RAG-based systems.
• Solid software engineering fundamentals including APIs, Docker, and CI/CD.

Responsibilities

• Design, develop, and deploy advanced machine learning models.
• Architect scalable LLMOps pipelines on GCP / Vertex AI.
• Collaborate with product and engineering teams to create ML solutions.

Skills

Machine learning engineering

Python

GCP Vertex AI

LLMs

Deep learning

Tools

PyTorch

TensorFlow

Hugging Face

Google GenAI

Docker

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