At a Glance
- Tasks: Design and develop innovative machine learning models for real-world challenges.
- Company: Leading tech business delivering advanced AI solutions for national security.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with cutting-edge technology and career advancement opportunities.
- Why this job: Join a pivotal growth stage and make an impact on critical defence outcomes.
- Qualifications: Master’s/PhD in relevant field and 3+ years of ML experience.
The predicted salary is between 36000 - 60000 £ per year.
Overview
Our client is a leading technology business delivering advanced AI solutions into highly complex, mission-critical environments. Their cross-functional product teams blend software engineering, machine learning, and deep domain expertise to deliver platforms that enable analysts and operators to work with speed, clarity, and confidence. This is an opportunity to join at a pivotal stage in growth, working on products that directly support national security and defence outcomes.
The Role
As an AI Engineer , you will design, develop, and deploy innovative machine learning models and algorithmic solutions. You’ll build production-grade AI capabilities that solve hard problems at scale, with a focus on Generative AI and modern NLP/ML workflows.
Responsibilities
- Design and develop ML models and algorithmic solutions for complex, real-world challenges.
- Partner with product managers, engineers, and domain experts to deliver features from concept through to production.
- Engineer solutions with a deep awareness of algorithmic complexity and cost of scale.
- Clearly communicate model choices, assumptions, and trade-offs to technical and non-technical stakeholders.
- Take ownership from experimentation through deployment, monitoring, and optimisation.
- Debug and maintain distributed data pipelines in production.
- Stay up to date with emerging ML/AI research and apply new techniques where valuable.
Skills & Experience
- Master’s/PhD in Computer Science, Machine Learning, NLP, or related field.
- 3+ years applying ML in production environments.
- Hands-on expertise with Generative AI (fine-tuning LLMs, RAG pipelines, agentic workflows).
- Strong Python development skills with frameworks like Hugging Face, spaCy, PyTorch, Scikit-learn.
- Knowledge of Kubernetes and ML model deployment at scale is an advantage.
- Exposure to systems integration (PostgreSQL, Elasticsearch, etc.) desirable.
- Strong communication skills and ability to bridge technical and product impact.
Seniority level
- Mid-Senior level
Employment type
- Contract
Industries
- IT Services and IT Consulting
- Defense and Space Manufacturing
- Data Infrastructure and Analytics
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AI Engineer in England employer: Frontier Resourcing
Join a leading high-tech defence organisation in Bristol, where innovation meets purpose. As a Systems Architect in Electronic Warfare, you will thrive in a collaborative work culture that values technical excellence and offers ample opportunities for professional growth. With a focus on cutting-edge technology and a commitment to employee development, this role provides a unique chance to contribute to vital defence systems while enjoying the benefits of a supportive and dynamic team environment.
StudySmarter Expert Advice🤫
We think this is how you could land AI 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. We all know that sometimes it’s not just what you know, but who you know that can land you that AI Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving Generative AI and NLP. We want to see your hands-on expertise in action, so make sure to highlight your best work when chatting with potential employers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python skills and understanding ML model deployment. We recommend practicing coding challenges and discussing your thought process out loud, as this will help you communicate effectively with both technical and non-technical stakeholders.
✨Tip Number 4
Don’t forget to apply through our website! It’s a great way to get noticed and shows your enthusiasm for the role. Plus, we’re always on the lookout for passionate candidates who are ready to take ownership of their projects from experimentation to deployment.
We think you need these skills to ace AI Engineer in England
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the AI Engineer role. Highlight your experience with machine learning models and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a great fit for our team. Don't forget to mention any specific experiences that relate to generative AI or NLP.
Showcase Your Projects:If you've worked on any interesting projects, especially those involving generative AI or ML workflows, make sure to include them in your application. We love seeing real-world applications of your skills!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Frontier Resourcing
✨Know Your Stuff
Make sure you brush up on your machine learning concepts, especially around Generative AI and NLP. Be ready to discuss your past projects in detail, focusing on the challenges you faced and how you overcame them.
✨Showcase Your Communication Skills
Since you'll need to communicate complex ideas to both technical and non-technical stakeholders, practice explaining your work in simple terms. Think about how you can convey your model choices and trade-offs clearly.
✨Demonstrate Problem-Solving Abilities
Prepare to discuss real-world problems you've solved using ML. Highlight your thought process, from experimentation to deployment, and be ready to talk about how you monitor and optimise your solutions post-deployment.
✨Stay Current with Trends
Familiarise yourself with the latest research and trends in AI and ML. Being able to discuss recent advancements or techniques that could apply to their projects will show your passion and commitment to the field.
