Platform Engineer (Machine Learning Operations)

Builder.ai - What would you Build?
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Platform Engineer - AI MLOps Oxford, England, United Kingdom

Senior AI MLOps Platform Engineer - Scale Resilient Cloud

Senior MLOps Platform Engineer — Cloud & Kubernetes

Senior Machine Learning Engineer

Data Engineer

Senior Data Engineer/ PowerBI

About Builder.ai

We’re on a mission to make app building so easy everyone can do it – regardless of their background, tech knowledge or budget. We’ve already helped thousands of entrepreneurs, small businesses and global brands, like NBC Universal and Pepsi, as well as American organizations like Bobcat and Smart Path, achieve their software goals. And we’ve only just started!
Builder.ai was voted as one of 2023’s ‘Most Innovative Companies in AI’ by Fast Company, and won Europas 2022 ‘Scaleup of the Year’. Our team has grown to over 800 people across the world and our recent announcement of $250m Series D funding (and partnership with Microsoft) means there’s never been a more exciting time to become a Builder.

Life at Builder.ai

At Builder.ai we encourage you to experiment! Each role at Builder has unlimited opportunities to learn, progress and challenge the status quo. We want you to help us become even better at supporting our customers and take AI app building to new heights.

Our global team is diverse, collaborative and exceptionally talented. We hire people for their differences but all unite with our shared belief in Builder’s HEARTT values: (Heart, Entrepreneurship, Accountability, Respect, Trust and Transparency) and a let’s-get-stuff-done attitude.

In return for your skills and commitment, we offer range of great perks, from hybrid working and a variable annual bonus, to employee stock options, generous paid leave, and trips abroad #WhatWillYouBuild

Why We Need This Role

We are looking for a Platform Engineer (Machine Learning Operations)  to help us maintain and expand an ecosystem of microservices developed within the Artificial IntelligenceGroup. We leverage a range of AI techniques including large scale knowledge graphs and Generative AI (LLMs for text and images). The role includes ensuring robustness and scalability of AI-based production APIs, as well as data and model training pipelines.  As we push the boundaries of what is possible, and scale our systems to improve the services we deliver to customers and internal teams, we need individuals that can help us innovate fast, but maintain a high quality bar.

Why You Should Join

The position will be at the intersection of data science and development operations. The candidate would want to join because of the extreme variety of problems we are facing: support to data scientists in the development of AI-based production APIs; support the design, implementation, deployment, maintenance of our microservices infrastructure, data and model training pipelines. This is an engaging role and the ideal candidate should be an eager problem solver that takes pride in the production of clean, robust and scalable solutions.

Our Tech Stack

  • Python
  • SQL
  • Cypher
  • Git/Gitlab and CI/CD workflows
  • Terraform
  • Docker and Kubernetes (kubectl, helm, helmfile)
  • AWS, Azure

Requirements

      • Computer Science or Software Engineering degree / BSc or higher
      • Strong Python coding experience
      • Experience with the SQL querying language
      • Experience working with Docker both in development and deployment environments
      • Experience reading/implementing CI/CD workflows
      • Experience with software engineering best practices: unit testing (with mocks), code reviews, design documentation, excellent debugging, troubleshooting skills
      • Excellent communication & drive to learn and experiment
      • Passionate about loosely held values and ideas. We want someone who has experience but is not blinded by the path already taken.
      • Makes decisions based on data and evidence.

Added bonus:

      • Background in running Kubernetes
      • Experience with Graph databases
      • Hands-on experience in all facets of automation and systems architecture, with particular focus on Linux and open source technologies
      • Experience with GitLab
      • Experience working in Cloud environments, in particular Azure or AWS

Benefits

    • Attractive quarterly OKR bonus plan or commission scheme dependant on your role
    • Stock options in a $450 million funded Series D scale-up company
    • 24 days annual leave + bank holidays
    • 2 x Builder family days each year
    • Time off between Christmas and New Year
    • Generous Referral Bonus scheme
    • Pension contributions
    • Private Medical Insurance provided by AXA 
    • Private Dental Insurance provided by Bupa 
    • Access to our Perkbox

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.