Lead Machine Learning Engineer - GenAI

Codesearch AI
Glasgow
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Machine Learning Engineer, Associate Director, London

Staff Machine Learning Engineer, Anomaly Detection United Kingdom, London

Staff Machine Learning Engineer Melbourne

Machine Learning Manager

Machine Learning Engineering Manager

Principal MLOps Engineer - Chase UK

An unsolved problem in a multi-billion-pound industry


A cash positive, revenue generating start-up with signed commitments


An opportunity to lead the build of a first-of-its kind AI platform utilising SOTA tools and techniques


We are looking for a Lead Machine Learning Engineer – GenAI to build a field-changing, cutting-edge AI platform. In an industry filled with complexity and inefficiency, there’s an opportunity to create an intelligence platform that doesn’t only eliminate waste, but ultimately impacts people in key aspects of everyday life.


Our client is ahead of the curve and fully invested in taking their approach and vision to the next level.


What You’ll Be Doing


Building a multi-model, cutting edge intelligence platform integrating text and image data with state-of-the-art generative models, alongside traditional techniques


Designing a data and document ingestion strategy for multi-format data


Selecting the most appropriate models and approaches, RAG techniques and tools


Design and execute the technical roadmap and architecture to build a scalable platform


Develop and fine-tune LLMs and design multi-step Agentic workflows


Implement feedback loops for model performance evaluation


Provide input on and oversee the development of Robust LLMOps & DevOps practices


Lead and grow the ML team, mentoring and hiring engineers to scale the platform


80/20 split of hands-on work, weighted toward building


What You’ll Need


MSc or PhD in Machine Learning, AI, Computer Science or a related field (or equivalent experience)


Strong foundations in NLP with ideally a minimum of 5 years’ industry experience in AI, Machine

Learning, Reinforcement Learning or similar field


Have experience building and scaling AI-first products, with technical leadership experience, ideally in a start-up environment


Industry experience with LLMs (fine-tuning, optimising, performance evaluation) and Retrieval-


Augmented Generation (RAG) techniques including document linking.


Experience with knowledge graphs and vector databases


Strong experience with Python and modern AI development frameworks


Expertise in MLOps/LLMOps/DevOps including deploying AI solutions at scale.


Knowledge of traditional databases and scalable architecture design


Person - Whilst you’ll be working on cutting edge techniques, we are looking for people that build according to the need


You’ll build with urgency but be pragmatic in your approach


Location - Ideally this role is onsite in Dubai but we will consider remote working from the UK or Europe for the ideal candidate

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.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.