Machine Learning Engineer

Stott and May
City of London
1 month 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

MLOps Engineer

Location: London, UK (Hybrid – 2 days per week in office)

Day Rate: Market rate (Inside IR35

Duration: 6 months

Role Overview

As an MLOps Engineer, you will support machine learning products from inception, working across the full data ecosystem. This includes developing application-specific data pipelines, building CI/CD pipelines that automate ML model training and deployment, publishing model results for downstream consumption, and building APIs to serve model outputs on-demand.

The role requires close collaboration with data scientists and other stakeholders to ensure ML models are production-ready, performant, secure, and compliant.


Key Responsibilities

  • Design, implement, and maintain scalable ML model deployment pipelines (CI/CD for ML)
  • Build infrastructure to monitor model performance, data drift, and other key metrics in production
  • Develop and maintain tools for model versioning, reproducibility, and experiment tracking
  • Optimize model serving infrastructure for latency, scalability, and cost
  • Automate the end-to-end ML workflow, from data ingestion to model training, testing, deployment, and monitoring
  • Collaborate with data scientists to ensure models are production-ready
  • Implement security, compliance, and governance practices for ML systems
  • Support troubleshooting and incident response for deployed ML systems


Required Skills and Experience

  • Strong programming skills in Python; experience with ML libraries such as Snowpark, PySpark, or PyTorch
  • Experience with containerization tools like Docker and orchestration tools like Airflow or Astronomer
  • Familiarity with cloud platforms (AWS, Azure) and ML services (e.g., SageMaker, Vertex AI)
  • Experience with CI/CD pipelines and automation tools such as GitHub Actions
  • Understanding of monitoring and logging tools (e.g., NewRelic, Grafana)


Desirable Skills and Experience

  • Prior experience deploying ML models in production environments
  • Knowledge of infrastructure-as-code tools like Terraform or CloudFormation
  • Familiarity with model interpretability and responsible AI practices
  • Experience with feature stores and model registries

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.

Careers

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.

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.

🍪 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.