National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

AI Software Engineer

Heddon on the Wall
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Software Engineer – API & ML Infrastructure

AI Engineering Researcher

Machine Learning Engineer

ML/Data Engineer - AI

Head of Engineering

Junior Machine Learning Engineer - AI startup

AI Software Engineer - Newcastle - Up to £60,000 + Bonus
Cutting-Edge AI & Computer Vision Software

KO2's client, an innovative and fast-growing technology company based in the Newcastle area, is looking to recruit an AI Software Engineer to develop next-generation computer vision systems for real-time applications. This is an exciting opportunity to join a highly skilled engineering team working on impactful AI solutions deployed in real-world environments.

Key Responsibilities:

Develop and implement advanced AI and machine learning models for computer vision applications.
Build and optimise real-time video processing pipelines using tools such as GStreamer and FFmpeg.
Train, validate, and refine AI models using best practices, with a focus on precision, recall, and other key performance metrics.
Write efficient, production-level code in Python and C++.
Evaluate and integrate state-of-the-art AI techniques to address complex computer vision challenges.Essential Requirements:

Bachelor's or Master's degree in Computer Science, Data Science, or a related technical discipline.
5+ years of hands-on experience working on computer vision problems and AI system development.
Strong programming skills in Python and C++.
Experience with real-time video pipelines, particularly GStreamer and FFmpeg.
Solid understanding of AI model training concepts (e.g., epochs, hyperparameters, training/validation datasets).
Demonstrated ability to apply the right computer vision techniques and critically evaluate their advantages.Desirable Skills:

Experience deploying AI software in edge computing environments, especially on Nvidia Jetson hardware.
Background in sectors such as automotive computer vision or other real-time, high-reliability fields.
Ability to design appropriate AI models based on a given problem statement and source data.What's on Offer:

A competitive salary up to £60,000 depending on experience.
A chance to work on cutting-edge AI projects with real-world applications.
Flexible and collaborative working environment, with hybrid or on-site options available.If you're ready to take the next step in your AI engineering career and want to work with a forward-thinking team delivering real innovation, apply today to KO2's client in Newcastle

National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.