At a Glance
- Tasks: Build AI features that turn messy data into structured insights for manufacturers.
- Company: Well-funded industrial AI scale-up with a focus on innovation.
- Benefits: Competitive salary, hybrid work model, and opportunities for direct ownership.
- Other info: Fast decision-making environment with excellent career growth potential.
- Why this job: Join a small team tackling real-world AI challenges that make a difference.
- Qualifications: Strong Python skills and experience with production AI systems required.
The predicted salary is between 60000 - 75000 £ per year.
A well-funded industrial AI scale-up is hiring a Senior AI Software Engineer as they expand their platform and respond to growing customer demand. They are building technology that turns messy manufacturing data into structured insight — helping factories understand and improve how things are made. With a rising workload, they are adding a hands-on engineer to accelerate delivery.
The Role
- Build and ship AI features that transform unstructured operational data into trustworthy, structured knowledge for manufacturers.
- Extend and maintain their knowledge graph platform, including adding a time dimension — a core technical challenge.
- Design and implement RAG and agentic systems that work reliably in industrial environments.
- Own end-to-end delivery: ingestion, embeddings, retrieval, ontology adjustments, deployment and monitoring.
- Work closely with a small engineering team (backend, frontend, architecture) to bring AI capabilities into production quickly.
- Tackle a 40-item AI backlog and help define how AI is built and scaled inside the organisation.
What You’ll Bring
- Strong Python engineering skills with experience delivering production AI systems (assumed FastAPI/Flask familiarity).
- Practical experience working with structured and unstructured data — beyond notebooks and prototypes.
- Understanding of RAG, embedding retrieval algorithms, self-refinement patterns and knowledge graph integration.
- Experience adjusting ontologies / column dictionaries for real-world data environments.
- Prior experience delivering in production.
- Familiarity with Neo4j or graph databases and cloud deployment environments.
What’s On Offer
- Salary (UK full-time): £60k–£75k.
- Hybrid London with periodic Prague visits.
- Small, focused team with direct ownership and fast decision-making.
- Work on applied AI challenges that matter.
Screening Questions
- What production systems have you built that work with unstructured or mixed-format data?
- Describe your experience with RAG, retrieval algorithms or knowledge graph integrations.
- Have you worked on systems requiring ontology or schema adjustments across different environments?
Artificial Intelligence Engineer employer: Wave Talent
Join a dynamic and innovative industrial AI scale-up that prioritises employee growth and collaboration. With a small, focused team in a hybrid London setting, you will have the opportunity to take ownership of impactful projects while enjoying a supportive work culture that encourages fast decision-making and direct contributions to meaningful AI solutions. The company offers competitive salaries and the unique advantage of periodic visits to Prague, enhancing both professional development and personal experiences.
StudySmarter Expert Advice🤫
We think this is how you could land Artificial Intelligence Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the AI field, especially those working at companies you're interested in. A friendly chat can open doors and give you insider info that could help you stand out.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving unstructured data or RAG systems. This gives potential employers a taste of what you can do and makes you memorable.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with Python and production AI systems, as well as how you've tackled real-world data challenges.
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to connect with exciting opportunities in the AI space. Plus, it shows you're serious about joining our team!
We think you need these skills to ace Artificial Intelligence Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of an Artificial Intelligence Engineer. Highlight your Python skills and any experience with production AI systems, especially if you've worked with unstructured data. We want to see how your background fits into our mission!
Showcase Relevant Projects:Include specific projects that demonstrate your experience with RAG, embedding retrieval algorithms, and knowledge graph integration. We love seeing real-world applications of your skills, so don’t hold back on the details!
Answer Screening Questions Thoughtfully:Take your time with the screening questions. They’re a chance for us to understand your hands-on experience with production systems and ontology adjustments. Be clear and concise, and give examples where possible!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you get all the updates directly from us. Plus, it shows you're keen on joining our team!
How to prepare for a job interview at Wave Talent
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and the frameworks mentioned, like FastAPI or Flask. Brush up on your experience with structured and unstructured data, as you'll likely be asked to discuss specific projects where you've delivered production AI systems.
✨Showcase Your Problem-Solving Skills
Be prepared to tackle questions about RAG, embedding retrieval algorithms, and knowledge graph integration. Think of examples from your past work where you faced challenges and how you overcame them, especially in real-world data environments.
✨Understand the Company’s Needs
Research the company’s platform and their approach to transforming manufacturing data. This will help you align your answers with their goals and demonstrate that you’re genuinely interested in contributing to their mission.
✨Prepare for Team Dynamics
Since you’ll be working closely with a small engineering team, think about how you can contribute to a collaborative environment. Be ready to discuss your experience in teamwork and how you’ve successfully delivered projects in a fast-paced setting.
