Lead Machine Learning Engineer - GenAI

Codesearch AI
Glasgow
10 months 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

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

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.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.