Machine Learning Engineer (Multimodal)

Harnham
London
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer - London

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer (Multimodal) Up to £100,000 London Hybrid/Remote Driving innovation with AI and machine learning to revolutionize financial services and enhance customer experiences. COMPANY Harnham has partnered with a leading Fintech company using advanced AI technology to transform financial services. Their cutting-edge approach has led to the development of innovative financial solutions, making significant strides in areas such as fraud detection, personalized financial advice, and risk management. ROLE: Lead the development of AI algorithms, focusing on AI/ML techniques and Large Language Models (LLMs) to drive innovation in financial services. Build and test machine learning models, advocate for best coding practices, and ensure high-quality results through thorough testing. Collaborate closely with data scientists, financial analysts, and engineers to develop and implement AI/ML tools for data analysis. Leverage expertise in multimodal LLMs, especially in search and retrieval-augmented generation (RAG) technologies, to enhance model performance and application in financial contexts. YOUR SKILLS AND EXPERIENCE: MSc or PhD in a STEM subject. Proven experience with the implementation of Machine Learning models and Large Language Models, including multimodal LLMs. MLOps/DevOps experience with CI/CD pipelines. Proficiency in TensorFlow, Kubernetes, MLFlow, Kafka, and Airflow. Strong Python skills are essential; experience with AWS and Spark is beneficial. Excellent communication skills and experience engaging with team members and stakeholders. Expertise in large-scale computation and experience in a research or tech-driven environment. Familiarity with LLMs and tools like Langchain, with specific exposure to search and retrieval-augmented generation (RAG) technologies. Keen interest in financial technology and the Fintech space. BENEFITS: Salary up to £100,000 Bonus Healthcare & Pension HOW TO APPLY: Please register your interest by sending your CV to Luc Simpson-Kent via the link on this page.

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.