AI Engineer (Machine Learning & Computer Vision)

In Technology Group
Leeds
1 month ago
Applications closed

Related Jobs

View all jobs

AI Engineer - Machine Learning LLM

AI Engineer - Machine Learning LLM

AI Software Engineer

Graduate AI Engineer

MLOps & AI Engineer Lead

Machine Learning and AI Engineering Lead

Job Title: AI Engineer (Machine Learning & Computer Vision)

Location: Leeds (3 Days a Week)

Salary: £60,000 - £80,000 + Benefits


About Us:

The Client, based in Leeds,have just received their Series A funding, is at the forefront of AI innovation, building cutting-edge solutions that harness the power of artificial intelligence, machine learning, and computer vision. Our team is dedicated to solving complex problems and delivering intelligent systems that drive real-world impact. We are seeking a skilled AI Engineer to help us push the boundaries of what's possible.


Responsibilities:


  • Design, develop, and deploy machine learning models, with a strong focus on computer vision applications.
  • Implement deep learning architectures using frameworks such as TensorFlow, PyTorch, or Keras.
  • Develop and optimize image processing and vision-based AI algorithms for object detection, segmentation, and classification.
  • Work with large-scale datasets, including data preprocessing, augmentation, and annotation.
  • Build and deploy AI models into production using cloud-based services (AWS, Azure, GCP) or edge computing platforms.
  • Improve model performance through hyperparameter tuning, transfer learning, and advanced optimization techniques.
  • Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to integrate AI models into applications.
  • Stay up to date with the latest AI research, trends, and emerging technologies in computer vision and deep learning.
  • Develop scalable APIs and integrate AI solutions with existing infrastructure.


Technical Skills:


  • Expertise in Python, C++, or Java for AI/ML development.
  • Proficiency in ML frameworks and libraries such as TensorFlow, PyTorch, OpenCV, Scikit-learn, and ONNX.
  • Experience with cloud platforms (AWS, Azure, GCP) for AI model deployment and scaling.
  • Strong understanding of neural networks, deep learning architectures, and computer vision techniques.
  • Knowledge of real-time AI inference and edge computing optimization.
  • Experience with AI pipeline automation, version control, and CI/CD integration.
  • Familiarity with data engineering tools such as Apache Spark, Hadoop, or SQL for large-scale data processing.
  • Proficiency in using Git, Docker, and Kubernetes for deployment and collaboration.


Required Qualifications:


  • Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
  • Strong proficiency in Python and experience with ML libraries/frameworks (TensorFlow, PyTorch, OpenCV, Scikit-learn).
  • Experience with computer vision techniques such as image classification, object detection (YOLO, Faster R-CNN), image segmentation, and OCR.
  • Knowledge of deep learning architectures, including CNNs, RNNs, and transformers.
  • Experience working with large-scale datasets, data augmentation, and feature extraction techniques.
  • Proficiency in cloud computing services for AI/ML deployment (AWS SageMaker, Azure ML, GCP AI).
  • Familiarity with MLOps practices, including model versioning, deployment, and monitoring.
  • Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.


Preferred Qualifications:


  • Experience with edge AI deployment (NVIDIA Jetson, TensorRT, OpenVINO, or Coral Edge TPU).
  • Hands-on experience with reinforcement learning or generative AI models.
  • Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
  • Experience with automated ML pipelines and tools like Kubeflow or MLflow.
  • Contributions to open-source AI/ML projects or research publications in AI conferences.


What We Offer:


  • Competitive salary and equity options.
  • Opportunities to work on cutting-edge AI technologies and impactful projects.
  • A collaborative, innovation-driven work environment.
  • Flexible work arrangements and remote work options.
  • Continuous learning and professional development support.


Desirable Benefits:

  • Health, dental, and vision insurance
  • Flexy days off (upto 40)
  • Generous paid time off, including vacation and sick leave.
  • Stock options and performance-based bonuses.
  • Relocation assistance for eligible candidates.
  • Access to state-of-the-art AI research labs and computing resources.
  • Sponsored attendance at AI/ML conferences and workshops.

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.

Rural-Remote Machine Learning Jobs: Finding Balance Beyond the Big Cities

Over the past decade, machine learning (ML) has transformed from a niche research domain into a pervasive technology underpinning everything from recommendation systems and voice assistants to financial forecasting and autonomous vehicles. Historically, the UK’s major tech hubs—particularly London—have been magnets for top ML talent and corporate headquarters. However, remote work has become mainstream, and many ML professionals are realising they can excel in their field while living far beyond the city limits. At MachineLearningJobs.co.uk, we’ve observed a growing interest in positions that allow for a rural lifestyle or a coastal environment, often reflected in search terms like “ML remote countryside” or “tech jobs by the sea.” This surge is no coincidence. Flexible work policies, better rural broadband, and the nature of machine learning tasks—much of which can be done through cloud platforms—are bringing new opportunities to those who wish to swap urban hustle for fresh air and scenic views. Whether you’re a data scientist, ML engineer, researcher, or product manager, a rural or seaside move could reinvigorate your work-life balance. In this article, we’ll unpack why rural-remote ML jobs are on the rise, how you can navigate the challenges of leaving the city, and what you need to do to thrive in a machine learning career beyond the M25. If you’ve dreamt of looking up from your laptop to rolling fields or ocean waves, keep reading—your rural ML role might be closer than you think.

Quantum-Enhanced Machine Learning—Propelling AI into the Next Frontier

Machine learning (ML) has revolutionised how we interpret data, build predictive models, and create intelligent applications. From recommendation engines and self-driving cars to advanced genomics and natural language processing, ML solutions are integral to nearly every corner of modern life. However, as data complexity and model size continue to skyrocket, the computational demands placed on ML systems grow in tandem—often pushing even high-performance classical computers to their limits. In recent years, quantum computing has emerged as a tantalising solution to these challenges. Unlike traditional digital systems, quantum computers exploit quantum mechanics—superposition and entanglement—to process information in ways that defy conventional logic. As these machines mature, they promise exponential speed-ups for certain tasks, potentially reshaping how we approach AI and data-intensive challenges. What does this mean for machine learning? Enter quantum-enhanced ML, a new frontier where quantum processors and classical ML frameworks unite to accelerate model training, tackle high-dimensional data, and solve complex optimisation tasks more efficiently. In this article, we will: Unpack the current state of machine learning, highlighting key bottlenecks. Provide a concise overview of quantum computing—why it’s radical and how it differs from classical technology. Examine potential breakthroughs in quantum-enhanced ML, including real-world use cases and technical approaches. Explore the roles and skill sets that will define this quantum-AI era, with guidance on how to prepare. Discuss the roadblocks (like hardware maturity and ethical concerns) and how they might be addressed in the years to come. If you’re a machine learning engineer, data scientist, or simply an AI enthusiast fascinated by the next wave of computational innovation, read on—quantum computing could become an integral part of your future toolkit, opening up job opportunities and reimagining what ML can achieve.

Machine Learning Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Machine learning (ML) has become the beating heart of modern tech innovation, powering breakthroughs in healthcare, finance, cybersecurity, robotics, and more. Across the United Kingdom, this surge in ML-driven solutions is fueling the success of countless start-ups—and spurring demand for talented machine learning engineers, data scientists, and related professionals. If you’re eager to join a high-growth ML company or simply want to keep tabs on the latest trends, this Q3 2025 Investment Tracker will guide you through the newly funded UK start-ups pushing the boundaries of ML. In this article, we’ll highlight key developments from Q3 2025, delve into the most promising newly funded ventures, and shed light on the machine learning roles they’re urgently seeking to fill. Plus, we’ll show you how to connect with these employers via MachineLearningJobs.co.uk, a dedicated platform for ML job seekers. Let’s dive in!