AI/ML Engineer

Austin Fraser International Ltd
London
1 year ago
Applications closed

Related Jobs

View all jobs

MLOps Engineer

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Graduate Machine Learning and AI Engineer

Graduate Machine Learning and AI Engineer

Data Engineering Product Owner, Technology, Data Bricks, Microsoft

AI/ML Engineer Exciting opportunity to join a leading technology firm in London as an AI/ML Engineer. If you have a strong background in AI and machine learning, particularly in generative models and large language models, we want to hear from you Location: Remote. Salary: £90,000-£110,000 Key Responsibilities: Develop, implement, and optimise AI/ML models focusing on large language models and generative AI. Collaborate with software engineers to integrate AI components into production systems. Design and maintain efficient machine learning pipelines for large-scale data processing. Deploy and manage AI/ML solutions using cloud platforms (AWS, Google Cloud). Build and maintain RESTful APIs to enable AI services for diverse applications. Engage in data preprocessing and feature engineering to ensure high-quality inputs for AI models. Conduct benchmarking and performance optimization to enhance model accuracy and efficiency. Participate in the entire software development lifecycle, including requirements gathering, design, implementation, testing, and deployment. Stay updated on the latest advancements in AI/ML and apply them to improve system capabilities. Qualifications: 3 years of experience in AI/ML engineering, with proven expertise in building large language models from scratch. Strong proficiency in Python and familiar with popular AI/ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn). Experience working with transformer-based architectures and LLMs. Knowledge of vector databases and experience with embeddings for applications like product recommendations. Proven experience using cloud services (AWS, Google Cloud) for machine learning model deployment. Hands-on experience in data processing techniques for AI/ML workflows. Understanding of CI/CD pipelines, version control systems (GitHub), and containerization (Docker, Kubernetes). Excellent problem-solving skills and experience troubleshooting complex AI/ML challenges, including deep learning and neural networks. Preferred Qualifications: Prior experience with fine-tuning large language models or generative models for specific tasks. Familiarity with distributed systems and parallel processing for large-scale training. Knowledge of messaging systems (e.g., Kafka, RabbitMQ). Understanding of various activation functions, loss functions, and neural network architectures. Strong communication and collaboration skills. Education: Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or a related field. If this exciting opportunity looks like it could be your next role, click apply now Austin Fraser is committed to being an equal opportunities employer, and encourages applications from candidates regardless of sex, race, disability, age, sexual orientation, gender reassignment, religion or belief, marital status, or pregnancy and maternity status. Due to the volume of applications received, we are unable to provide individual feedback to unsuccessful applicants. Check us out on our website and LinkedIn for more roles. We respect your personal data and would never offer it to third parties For more information on how we handle your data, feel free to check out the Austin Fraser Privacy Notice or contact privacyaustinfraser.com Austin Fraser International Ltd is registered in England: 14971372 Austin Fraser International Ltd, 33 Soho Square, London, W1D 3QU

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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.