Principle Engineer

Lime Street
1 week ago
Create job alert

A leading global insurance business is seeking a seasoned AI Leader capable of scaling AI capabilities to transform business models. This pivotal role will oversee a dynamic team responsible for the conception and execution of AI, ML, NLP, and Generative AI solutions, aimed at influencing business outcomes. Your expertise will be used to harness cutting-edge AI capabilities, multiple internal and external data sources to create new AI enabled business models and help businesses improve, profitability, sales, customer experience and operational efficiency.

Key Responsibilities

Your core responsibilities will be to drive AI based solutions, heading all stages of analytics initiatives, recommending, and implementing apt AI and computational methodologies, working with domain experts and business leads. You will lead a team of Machine Learning engineers, producing trail-blazing techniques in deep learning, NLP, and Generative AI.

Qualifications

Essential Skills/Experience

  • 10+ years of experience leading an AI team with hands-on implementation experience with a positive impact to business.

  • Deep understanding of Generative AI, Large Language Models, NLP, deep learning models and model implementation is a must.

  • Top-notch problem-solving skills, quick adaptability, and excellent communication are key.

  • You are expected to have a minimum of 10 years leading a team of ML Engineers, Data Scientists, developing deep learning models, with a solid understanding of recent Generative AI techniques. Familiarity with the P&C industry is preferred.

    Education

  • A PhD or Master’s degree in a field such as Computer Science, Computational Science, Statistics or related fields preferred

  • Relavant Insurance experience

Related Jobs

View all jobs

Senior Principle Electrical Engineer

Senior Principle Electrical Engineer

Senior Electrical Design Engineer - Hybrid Working

Principle Technologist - Chiswick 2 days - 118k

Senior Data Scientist (MLOps)

Data Operations Engineer

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.

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.

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.

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.