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- Tasks: Design and deploy LLM-powered systems to tackle real operational challenges.
- Company: Join a pioneering Series A startup focused on innovative AI solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Be at the forefront of AI technology and make a tangible impact.
- Qualifications: Experience in building production AI systems and strong machine learning foundations.
- Other info: Inclusive culture encouraging diverse talent to apply and thrive.
The predicted salary is between 28800 - 48000 £ per year.
We're partnered with a Series A startup creating a new category of AI software that helps companies automate operations by capturing and operationalising their institutional knowledge. The team is engineering-led, with a culture built on technical rigor, ownership, and sustainable impact. This role sits at the intersection of applied AI research and production engineering - a role that is ideal for builders who want to push the boundaries of what's possible with LLMs while shipping real systems that customers depend on.
The Opportunity: As AI Engineer, you'll be responsible for the design and deployment of LLM-powered systems that solve genuine operational challenges. Your work will span the full development cycle: from prototyping and experimentation to production deployment, monitoring, and continuous improvement. You'll need to be versatile - you might be refining an evaluation harness; the next, scaling async workflows or investigating why an agent made an unexpected decision in production.
Responsibilities:
- Design and implement LLM-based systems and agentic workflows that solve real customer problems
- Build robust evaluation frameworks to measure, track, and improve model behavior
- Develop scalable asynchronous systems for AI orchestration and task execution
- Work on RAG pipelines, multi-agent architectures, and production LLM infrastructure
- Collaborate across engineering, research, and product to define what gets built
- Shape AI strategy and contribute to long-term technical direction
About You:
- Proven experience building and shipping production AI systems (not just prototypes)
- Strong foundations in machine learning and deep learning principles
- Hands-on experience with LLMs, agents, RAG systems, or large-scale inference pipelines
- Comfort moving between research papers and production codebases
- Natural curiosity, strong sense of ownership, and drive for continuous improvement
Research indicates that men will apply to a role when they only meet 50-60% of the descriptions, however, when looking at women and other minority groups, they can look for up to a 99% match in order to apply to a role. If you feel you are a fit for our role, please still apply, don’t worry if you don’t tick every single box. We’d still love to hear from you. We encourage underrepresented talent to apply to all our roles.
AI Engineer employer: Primis
Contact Detail:
Primis Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the AI field, especially those working at startups. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs or production AI systems. This gives you a chance to demonstrate your hands-on experience.
✨Tip Number 3
Prepare for technical interviews by brushing up on your machine learning and deep learning principles. Practice coding challenges related to AI systems to get into the right mindset.
✨Tip Number 4
Don’t forget to apply through our website! We’re keen to see diverse talent, so even if you don’t tick every box, we want to hear from you. Your unique perspective could be just what we need!
We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Show Your Passion for AI: When writing your application, let your enthusiasm for AI shine through! Share specific examples of projects or experiences that highlight your love for building and deploying AI systems. We want to see your excitement for pushing boundaries!
Tailor Your Application: Make sure to customise your application to reflect the job description. Highlight your experience with LLMs and production AI systems, and don’t forget to mention any relevant projects. We appreciate when candidates take the time to connect their skills to what we’re looking for.
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to describe your experiences and achievements. We value clarity, so make it easy for us to see how you fit into our team!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Primis
✨Know Your AI Stuff
Make sure you brush up on your machine learning and deep learning principles. Be ready to discuss your hands-on experience with LLMs and how you've built and shipped production AI systems. This is your chance to show off your technical knowledge!
✨Show Your Problem-Solving Skills
Prepare examples of real operational challenges you've tackled in the past. Think about how you designed and implemented LLM-based systems or improved model behaviour. Being able to articulate your thought process will impress the interviewers.
✨Be Versatile and Curious
This role requires a mix of skills, so be ready to talk about your adaptability. Share experiences where you moved between research and production codebases, and highlight your natural curiosity and drive for continuous improvement. They want builders who push boundaries!
✨Collaborate Like a Pro
Since this role involves working across engineering, research, and product teams, think of examples where you've successfully collaborated with others. Emphasise your communication skills and how you contributed to shaping AI strategy in previous roles.
