At a Glance
- Tasks: Shape AI design and deployment in a live cybersecurity product environment.
- Company: Join a growing tech business at the forefront of AI innovation.
- Benefits: Competitive salary, remote work, and opportunities for professional growth.
- Why this job: Make a real impact in AI R&D while working on cutting-edge projects.
- Qualifications: Strong AI/ML engineering skills and experience with Python and cloud platforms.
- Other info: High ownership role with excellent career advancement potential.
The predicted salary is between 75000 - 95000 £ per year.
You will join a growing technology business at a pivotal stage in its AI journey. These are the first dedicated AI engineering hires in the team, so you will have the chance to shape how AI is designed, built, and deployed across a live cybersecurity product environment.
Initially, your focus will sit within the cyber division, where you will work on AI-driven initiatives tied to vulnerability management, penetration testing workflows, remediation checking, intelligent reporting, and automation. Longer term, there is scope for the role to expand into wider product areas across the group.
This is a hands-on engineering role with real ownership. You will take AI projects from concept through to production, not just build prototypes and hand them over. You will be expected to design, develop, deploy, monitor, and maintain scalable AI services that integrate into existing platforms via APIs and microservices.
What They’re Looking For:
- The key requirement is strong, real-world AI engineering capability. They need someone who can build and deliver, not someone who has only experimented on the edges of AI.
- You will ideally bring:
- AI/ML engineering experience in production environments
- LLM development and orchestration experience
- Python development
- Cloud platform experience across AWS or Azure or GCP
- End-to-end delivery experience from idea and prototyping through to deployment and support
- Experience building scalable services and APIs
- Strong communication skills and the ability to work closely with developers and stakeholders
- A self-starting approach with the confidence to own your workload and move initiatives forward
- Cybersecurity knowledge is not essential. That can be taught. The non-negotiable is deep AI and machine learning expertise.
What You’ll Work With:
You will work across a modern AI and product environment, with plenty of room to influence standards and tooling as the function matures. Likely technologies and themes include:
- Python development
- OpenAI models
- Anthropic models
- AWS Bedrock
- LLM workflows
- Agentic AI systems
- Machine learning algorithms
- API-led microservices
- Cloud platforms
- Monitoring, management, and alerting
- Vulnerability management workflows
- AI-assisted report generation
- Security testing automation
The current product direction is centred on building AI capabilities as services outside the main platform, then integrating them back in via APIs. That means the work has a genuine R&D feel, but always with a clear path into production.
Nice to Haves:
- Cybersecurity experience
- Penetration testing exposure
- Vulnerability management knowledge
- Agent-based system design
- Content analysis or anomaly detection experience
- MLOps understanding
- Enterprise environment experience
- Change control awareness
- KPI or ROI tracking experience
- Leadership or mentoring capability
- Product or solutions thinking
Why Join / Projects:
You will be joining very early in the AI build-out, which means high ownership, a broad remit, and the chance to make a visible impact.
Early projects are expected to include:
- AI-powered remediation checking following penetration tests
- Worker or agent-style services that perform specific testing tasks and report findings back
- LLM-powered reporting and consultant support tools
- AI modules for external, web, cloud, and later internal testing use cases
- Statistical analysis and machine learning models for wider business applications over time
- Reusable AI services that can eventually support multiple products and business units
This role will suit someone who enjoys solving complex problems, working in ambiguity, and building things properly from the ground up. There is likely to be a blend of seniority across the hires, so candidates with leadership potential or experience guiding others will be particularly valuable.
You will report initially into the cyber product and technology function, with close collaboration across product, architecture, engineering, and technical leadership.
Machine Learning Engineer in England employer: Prism Digital
Contact Detail:
Prism Digital Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in England
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. The more people you know, the better your chances of landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those related to machine learning and cybersecurity. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions and real-world scenarios. Practice explaining your thought process and problem-solving approach, as communication is key in this role.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Tailor your application to highlight your relevant experience and passion for AI and machine learning.
We think you need these skills to ace Machine Learning Engineer in England
Some tips for your application 🫡
Show Off Your Skills: When you're writing your application, make sure to highlight your real-world AI engineering experience. We want to see how you've taken projects from concept to production, so don’t hold back on the details!
Tailor Your Application: Make your application stand out by tailoring it to the role. Use keywords from the job description, like 'Python development' and 'cloud platforms', to show us you’re a perfect fit for our Machine Learning Engineer position.
Be Yourself: We love authenticity! Don’t be afraid to let your personality shine through in your application. Share your passion for AI and how you approach problem-solving – we want to know what makes you tick!
Apply Through Our Website: For the best chance of success, make sure to apply through our website. It’s the easiest way for us to keep track of your application and ensures you get all the latest updates from us!
How to prepare for a job interview at Prism Digital
✨Know Your AI Stuff
Make sure you brush up on your AI and machine learning knowledge, especially in production environments. Be ready to discuss your hands-on experience with LLMs, Python development, and cloud platforms like AWS or Azure. They want someone who can build and deliver, so come prepared with examples of your past projects.
✨Showcase Your Problem-Solving Skills
This role is all about solving complex problems, so be ready to share specific instances where you've tackled challenges in AI engineering. Think about how you approached a problem, the steps you took, and the impact of your solution. This will demonstrate your ability to work in ambiguity and build things from the ground up.
✨Communicate Clearly
Strong communication skills are key for this position. Practice explaining your technical projects in a way that non-technical stakeholders can understand. You might be asked to collaborate closely with developers and other teams, so showing that you can bridge the gap between tech and business will set you apart.
✨Be Ready to Discuss Future Projects
Since this role involves shaping the future of AI within the company, think about how you can contribute to upcoming projects. Familiarise yourself with their current product direction and be prepared to share your ideas on how to enhance AI capabilities, especially around automation and reporting tools.