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- Tasks: Design and build AI systems that impact millions of users across various industries.
- Company: High-growth AI scale-up focused on innovative intelligent systems.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Why this job: Join a technical team creating real-world AI solutions with cutting-edge technology.
- Qualifications: Strong software engineering skills and experience in product development.
- Other info: Dynamic environment with a focus on collaboration and continuous learning.
The predicted salary is between 60000 - 80000 £ per year.
A high-growth AI and analytics scale-up is building next-generation intelligent systems that help global enterprises better understand and act on customer behaviour at scale. This is an opportunity to join a deeply technical, product-focused engineering team working on real-world, production AI systems — not just prototypes — leveraging LLMs, agentic workflows, and modern cloud infrastructure.
You’ll play a key role in bridging the gap between software engineering and applied AI, building systems that directly impact millions of users across multiple industries.
As an AI Engineer, you’ll design, build, and deploy agent-driven software systems that turn cutting-edge AI capabilities into reliable, scalable products. This is a hands-on, end-to-end role, covering everything from prototyping through to production deployment and iteration.
Key Responsibilities- Build scalable, production-grade agentic systems and AI-powered applications
- Design and develop end-to-end features from concept to deployment
- Develop and maintain data pipelines, APIs, and CI/CD workflows
- Work closely with product and design teams to deliver user-facing functionality
- Apply best practices across performance, scalability, and maintainability
- Contribute to technical discussions, code reviews, and knowledge sharing
- Strong software engineering fundamentals (SDLC, testing, system design)
- Experience taking products from development → production
- Familiarity with cloud infrastructure and deployment pipelines
- Exposure to or strong interest in agentic systems / LLM applications
- Product mindset — focused on impact, iteration, and delivery
- Curious, adaptable, and comfortable working in an evolving AI landscape
- Experience with data pipelines or data engineering
- Understanding of large language models (LLMs) and real-world applications
- Exposure to modern AI tooling, agents, or workflow orchestration frameworks
You’ll work across a modern, AI-first stack including:
- Agent Frameworks: MCP (Model Context Protocol), agent-to-agent systems, emerging agent tooling
- Frontend: TypeScript, React, Next.js
- Backend: Python, Node.js
Artificial Intelligence Engineer employer: Intent HQ
Contact Detail:
Intent HQ Recruiting Team
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, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to agentic systems or LLM applications. This gives you a chance to demonstrate your hands-on experience and technical prowess.
✨Tip Number 3
Prepare for interviews by brushing up on your software engineering fundamentals and understanding the latest trends in AI. Practice coding challenges and be ready to discuss how you've taken products from development to production.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Artificial Intelligence Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the AI Engineer role. Highlight your software engineering fundamentals and any experience with LLM applications or agentic systems. We want to see how you can bridge the gap between software engineering and applied AI!
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 your product mindset and adaptability in the evolving AI landscape.
Showcase Your Projects: If you've worked on relevant projects, whether personal or professional, make sure to include them. We love seeing real-world applications of your skills, especially if they involve building scalable systems or using modern AI tooling. It gives us a glimpse into your hands-on experience!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re genuinely interested in joining our team at StudySmarter!
How to prepare for a job interview at Intent HQ
✨Know Your AI Fundamentals
Brush up on your software engineering fundamentals, especially around the software development lifecycle (SDLC), testing, and system design. Be ready to discuss how these principles apply to building scalable AI systems, as this will show your technical depth.
✨Showcase Your Project Experience
Prepare to talk about specific projects where you've taken products from development to production. Highlight your role in designing and deploying agent-driven systems or LLM applications, as this directly aligns with what the company is looking for.
✨Familiarise Yourself with Their Tech Stack
Get to know the technologies mentioned in the job description, like Python, Node.js, and cloud infrastructure. If you have experience with data pipelines or modern AI tooling, be sure to mention it, as it can set you apart from other candidates.
✨Emphasise Your Product Mindset
Demonstrate your understanding of the product lifecycle and your focus on impact, iteration, and delivery. Share examples of how you've collaborated with product and design teams to deliver user-facing functionality, as this shows you're a team player who values collaboration.