At a Glance
- Tasks: Design and deploy AI systems that tackle real-world challenges using cutting-edge technology.
- Company: Join a dynamic team working with top tech consultancies and software firms.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Exciting projects with multiple clients and excellent career advancement potential.
- Why this job: Be at the forefront of AI innovation and make a tangible impact on businesses.
- Qualifications: Experience in AI/ML systems, Python proficiency, and teamwork skills.
The predicted salary is between 60000 - 80000 £ per year.
We are currently partnering with multiple clients, ranging from innovative technology consultancies to fast-growing software organisations, who are looking for experienced AI Engineers to join their high-performing teams. This is a unique opportunity to design, build, and deploy production-grade artificial intelligence systems that solve complex, real-world business challenges at scale.
In this position, you will own the end-to-end lifecycle of AI-powered features, utilising advanced frameworks such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic workflows. You will translate high-level requirements into scalable, reliable technical architectures, working closely with product managers, data scientists, and software engineers to ensure seamless integration into existing product ecosystems. Furthermore, you will champion robust MLOps and LLMOps practices, implementing rigorous monitoring, evaluation frameworks, and CI/CD pipelines to ensure your solutions remain performant, secure, and maintainable.
What We're Looking For
- Professional experience in building and deploying production-grade AI/ML systems.
- Strong proficiency in Python and modern software engineering practices.
- Hands-on experience with Large Language Models (LLMs) and Generative AI.
- Expertise in designing and implementing RAG pipelines and agentic workflows.
- Proven experience with cloud platforms (AWS, GCP, or Azure) and containerisation technologies like Docker and Kubernetes.
- A strong understanding of MLOps/LLMOps, including model evaluation, monitoring, and observability.
- Experience working with vector databases and complex data processing pipelines.
- The ability to collaborate effectively within cross-functional teams and a product-focused mindset.
If you are a proactive problem-solver ready to drive innovation at the forefront of the AI landscape, we would love to hear from you. As we work with multiple clients, we often have several similar opportunities available, so please submit your CV and we will be in touch to discuss the best fit for your career goals.
AI Engineer employer: Myn
Join a dynamic and innovative environment where your expertise as an AI Engineer will be valued and nurtured. Our company fosters a collaborative work culture that encourages continuous learning and professional growth, offering access to cutting-edge technologies and projects that challenge the status quo. With a focus on employee well-being and a commitment to diversity, we provide a supportive atmosphere that empowers you to make a meaningful impact in the rapidly evolving field of artificial intelligence.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI field and let them know you're on the lookout for opportunities. Attend industry meetups or webinars to meet potential employers and showcase your skills.
✨Tip Number 2
Show off your projects! Create a portfolio that highlights your experience with AI/ML systems, especially those involving LLMs and RAG pipelines. This will give you an edge when chatting with hiring managers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python skills and understanding MLOps practices. Practice coding challenges and be ready to discuss your past projects in detail.
✨Tip Number 4
Don't forget to apply through our website! We have multiple clients looking for talented AI Engineers, and applying directly can help us match you with the right opportunity faster.
We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with AI/ML systems and showcases your proficiency in Python. We want to see how your skills align with the role, so don’t be shy about mentioning those Large Language Models and cloud platforms you've worked with!
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 you can contribute to our team. We love seeing candidates who can connect their experiences to the challenges we tackle at StudySmarter.
Showcase Your Projects:If you've worked on any relevant projects, whether personal or professional, make sure to include them. We’re keen to see examples of your work with RAG pipelines or MLOps practices, as this will give us insight into your hands-on experience.
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 all the exciting opportunities we have available. Plus, it makes the process smoother for everyone!
How to prepare for a job interview at Myn
✨Know Your AI Stuff
Make sure you brush up on your knowledge of AI/ML systems, especially around Large Language Models and RAG pipelines. Be ready to discuss specific projects you've worked on and how you tackled challenges in those areas.
✨Show Off Your Coding Skills
Since strong proficiency in Python is a must, practice coding problems that relate to AI engineering. You might be asked to solve a problem on the spot, so being comfortable with coding under pressure will definitely give you an edge.
✨Understand MLOps and LLMOps
Familiarise yourself with MLOps practices, including model evaluation and CI/CD pipelines. Be prepared to explain how you've implemented these in past roles, as this shows you can maintain and monitor AI systems effectively.
✨Collaboration is Key
Highlight your experience working in cross-functional teams. Share examples of how you've collaborated with product managers and data scientists to deliver successful AI solutions, as teamwork is crucial in this role.
