Principal Machine Learning Engineer - Team Lead

Qodea Group.
Swindon
1 year ago
Applications closed

Related Jobs

View all jobs

Principal Machine Learning Engineer - Production Systems

Principal Machine Learning Engineer

Principal Machine Learning Engineer – Production Systems

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Machine Learning Engineer - Xcede

As a Principal Machine Learning Engineer for Qodea, Europe's largest dedicated Google Cloud partner, you will lead the design and delivery of Machine Learning solutions for clients in addition to performing line management duties for a team of ML Engineers and Data Scientists. You'll report into the Head of Data & AI.

You’ll develop and deploy innovative machine learning models and AI solutions on Google Cloud using frameworks such as TensorFlow, scikit-learn, and torch. You’ll use your hands-on experience of developing, training, and deploying AI models to help customers activate their data.

You’ll draw upon your technical expertise and track record of delivery to have positive and meaningful engagements with customers, to help them understand what’s achievable with Google Cloud products and services. You’ll be able to communicate concepts to both technical and non-technical audiences.

Your solution designs will always consider the customers’ requirements, and will be scalable and supportable. You’ll always be open to exploring new technologies in this fast-moving field and will foster an innovative and creative mindset among the wider ML Team.

Your Line Management duties will consist of regularly engaging with your team members, enabling their professional development and being a consistent source of encouragement and support during their time with the organisation. You'll also foster a positive and collaborative environment within the Machine Learning team through remote and in-person meetups, social events, and knowledge-sharing activities.

Responsibilities:

  • Lead discussions with clients to understand their business problems, work with your team to design technical solutions using machine learning models.
  • Develop and deploy machine learning models on Google Cloud.
  • Use version control and agile working practices.
  • Stay up-to-date with the latest developments in machine learning and bring new ideas to the team.

Minimum Requirements:

  • Experience as a technical lead on technical projects, ideally involving a public cloud provider.
  • Experience with cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
  • Strong grasp of statistics and probability fundamentals.
  • Solid understanding of machine learning algorithms for supervised and unsupervised learning.
  • Hands-on experience training, deploying, and optimizing ML models.
  • Strong Python and SQL skills.
  • Experience with Cloud ML tools.
  • Collaborative, proactive, logical, methodical, and attentive to detail.
  • Passion for machine learning and demonstrating your ability to keep updated on the latest advancements in the industry.

About Qodea

Qodea (formally Appsbroker CTS) is the largest Google Cloud-only digital consultancy in Europe. Our name marks the culmination of a journey which began with the merger of Appsbroker and CTS in 2023. Combining the words ‘code’ and ‘idea’, our name embodies the essence of who we are and what we do; providing tried and trusted digital solutions, whilst helping our clients look to the future and innovate.

As a purpose-driven, certified B Corp, we strive to be great to work with and great to work for. We’re lucky to have some fantastic household names as customers, and fantastic colleagues delivering the ideas, technologies, and impacts that matter.

With offices across Europe, you’ll be joining a dynamic team of talented but down-to-earth experts, with a presence across the UK, the Netherlands, Romania, and Belgium.

By joining forces, both companies bring over 15 years of Google Cloud experience under one roof, with over 420+ Google certifications, a list of brilliant enterprise customers, incredibly talented people, and multiple industry awards - meaning we can be trusted to deliver.

Benefits:

  • 36 days off each year including Bank Holidays (and your birthday off).
  • Private healthcare scheme.
  • Company pension.
  • Flexible working culture.
  • Work from Anywhere policy (up to 90 days per year).
  • 10 paid Learning Days each year in addition to annual leave.
  • Company events - opportunities to meet colleagues you don’t see every day.
  • Regular opportunities for industry recognised training and certifications.
  • Learning and development opportunities.
  • Opportunities to develop within a fast-growing tech business with ambitious growth and impact goals.

Location:

This role can be either fully remote or hybrid based depending on your preference. We have offices in London, Swindon, Manchester, and Edinburgh which you can choose to work from as often as you like. There is no mandatory office working, but you might be expected to travel to offices or customer sites for specific meetings or events.

Diversity and Inclusion Statement

At Qodea, we look after each other in an environment where everyone can work together to achieve great things. We’re proud of our people-first culture that welcomes individuals from all backgrounds. Our commitment to diversity and inclusion creates a dynamic community, unlocks innovation and great ideas, and unites us around a common purpose - and we look for talented people to join us who share these values.

#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.

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.

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.