Machine Learning Engineer

Brio Digital
Birmingham
2 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer - Bristol

Audio Machine Learning Engineer

Senior Machine Learning Engineer (Generative AI / LLMs)

Location: Fully Remote (UK-based)


Salary: £75,000 – £100,000 (depending on experience)


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
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 up to £100k , plus benefits.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.