Lead Engineer, MLOps (London)

Code and Theory
London
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Software Engineer - MLOps

Lead Software Engineer - MLOps Platform

Lead MLOps Engineer

Lead MLOps Engineer

Lead Software Engineer - Agentic AI/Machine Learning

Senior Machine Learning Engineer (Platform) - Bristol

We are seeking an experienced Lead ML+DevOps Engineer. The ideal candidate will have strong expertise in cloud deployment, containerization, and related technologies, and will play a crucial role in the scalability and reliability of our AI/ML infrastructure.

WHAT YOU'LL NEED

Extensive experience in deploying machine learning models to cloud environments


Strong expertise in Docker container orchestration
Proficiency in Terraform for infrastructure as code (IaC) and cloud resource management
Hands-on experience with streaming data platforms (e.g., Kafka, Kinesis)
Solid understanding of data cleaning, transformation, and ETL processes
Experience with CI/CD tools and pipelines (e.g., Jenkins, GitLab CI)
Strong programming skills in Python. Familiarity with ML frameworks (e.g., TensorFlow, PyTorch) is a plus
Excellent problem-solving skills and the ability to think critically and creatively
Strong communication skills with the ability to convey technical concepts to non-technical stakeholders

ABOUT US


Born in 2001, Code and Theory is a digital-first creative agency that sits at the center of creativity and technology. We pride ourselves on not only solving consumer and business problems, but also helping to establish new capabilities for our clients. With a global client roster of Fortune 100s and start-ups alike, we crave the hardest problems to solve. We have teams distributed across North America, South America, Europe, and Asia. The Code and Theory global network of agencies is growing and includes Kettle, Instrument, Left Field Labs, Create Group, Mediacurrent, Rhythm, and TrueLogic.


Striving never to be pigeonholed, we work across every major category: from tech to CPG, financial services to travel & hospitality, government and education to media and publishing. We value the collaboration with our client partners, including but not limited to Adidas, Amazon, Con Edison, Diageo, EY, J.P. Morgan Chase, Lenovo, Marriott, Mars, Microsoft, Thomson Reuters, and TikTok.


The Code and Theory network is comprised of nearly 2,000 people with 50% engineers and 50% creative talent. We’re always on the lookout for smart, driven, and forward-thinking people to join our team.

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.