Lead Machine Learning Engineer

London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer – LLMs - Ramboll Tech

Lead ML Engineer
London - Hybrid
£80,000 - £95,000 + Bonus + Pension + Car Scheme + Technology Benefits + EAP Programme + Flexible working

This is an incredible opportunity for a Lead ML Engineer to join a fast paced and forward-thinking business always looking to innovate and lead from the front in the technology world.

The company are a leading organisation in the energy sector, dedicated to delivering innovative solutions and improving operational efficiency. As part of their Data Science team, you will be at the forefront of cutting-edge projects, helping to shape the future of data-driven decision-making and machine learning infrastructure.

In this role, you will lead machine learning projects from concept to production, develop platform tools, and collaborate with data scientists to build data pipelines. You'll mentor junior team members, work with IT teams to advance projects, and improve deployment processes. Additionally, you'll design and maintain cloud infrastructure, ensure high-quality code, and participate in code reviews.

The ideal candidate will have hands-on industry experience in software engineering, machine learning engineering, data science, DevOps, and cloud infrastructure. They will be highly proficient in Python, including libraries such as Pandas and scikit-learn, and possess strong SQL skills. A deep understanding of software engineering best practices and hands-on experience with tools like Azure, GitHub Actions, Terraform, Packer, Airflow, Docker, Kubernetes, and Linux/Windows VM administration is essential.

An incredible opportunity for a confident and commercial ML Engineer to lead from the front working with cutting edge technology and driving company growth.

The Role:

Lead machine learning projects from concept to production.
Develop platform tools and collaborate with data scientists to build data pipelines.
Mentor junior team members and support their technical growth.
Work closely with IT teams to advance project goals and improve deployment processes.
Design and maintain cloud infrastructure to support machine learning initiatives.
Ensure high-quality code and actively participate in code reviews to maintain code quality.
The Person:

Hands-on industry experience in software engineering, machine learning engineering, data science, DevOps, and cloud infrastructure.
Proficiency in Python, including libraries such as Pandas and scikit-learn, and strong SQL skills.
Deep understanding of software engineering best practices.
Experience with tools like Azure, GitHub Actions, Terraform, Packer, Airflow, Docker, and Kubernetes.
Expertise in Linux/Windows VM administration.
Solid knowledge of modern security and networking principles

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Careers

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Careers

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.

🍪 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.