Machine Learning Engineer

Bristol
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer - Bristol

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer
9 Month Contract
Location: Home / Bristol (3 days a week on site)
Rate: £750 - £800 per day (Inside IR35)

Skills: Machine Learning, Containerisation - Kubernetes, Docker, CI/CD Pipelines, SC Clearance

We are looking to recruit a Machine Learning Engineer for leading IT Software & Solutions organisation. This is an initial 9 month contract.

Due to the work valid SC Clearance is essential.

You will also be required to work on client site in Bristol 3 days a week.

Key Responsibilities:

Set Up & Configure ML Environments: Deploy and manage ML environments using tools like Kubernetes and Docker.
Automation & Workflow Optimization: Develop scripts for automation and ensure reproducibility of ML experiments.
Performance Monitoring: Conduct regular model performance reviews, data audits, and troubleshoot model-related issues.
Cross-functional Collaboration: Work within cross-functional teams to establish ML development best practices and secure CI/CD pipelines.
Scalable, Secure Solutions: Develop robust, secure, and scalable solutions while adhering to MOD and high-assurance compliance standards.
Innovative ML Development: Identify opportunities for reusable solutions to maximize return on development investments.Essential Skills:

Technical & Problem-Solving Skills: Strong analytical skills for logical solution analysis and troubleshooting.
ML Environment Experience: Proficient with Linux/Windows, ML frameworks (e.g., TensorFlow, PyTorch), and automation tools.
Programming Proficiency: Expertise in Python, Ruby, Perl, Java, with advanced scripting skills in Bash or PowerShell.
Model Monitoring & Performance Evaluation: Experience with MLflow, Prometheus, and similar tools for monitoring and logging.
DevOps & Agile Awareness: Familiar with DevOps, Agile principles, CI/CD pipelines, and version control (Git).
Security & Compliance: Understanding of secure code practices, threat modelling, and adherence to regulatory standards for high-assurance software.Additional Experience:

Industry Background: Over 5 years in defence, aviation, or medical sectors within roles such as software, DevOps, DevSecOps, MLOps, or AI engineering.
Complex Project Experience: Proven experience with software and AI development and deployment in complex, high-stakes environments.
Technical Documentation: Strong skills in producing high-standard technical documentation.Desirable Skills:

Data Project Development: Experience with large-scale data project implementation and solution governance.
Frameworks & Infrastructure Knowledge: Familiarity with SaaS, IaaS, PaaS, SOA, APIs, microservices, and predictive analytics.Qualifications:

Essential: Degree or equivalent in Software/AI, or relevant experience.
Desirable: Certifications in software languages, vendor qualifications (e.g., MCITP, VCP), and Agile/SAFe qualifications

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.

Jobs

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.

Careers Jobs

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.

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.