At a Glance
- Tasks: Build and evolve AI-powered services using Python and improve system quality.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with cross-functional teams and exciting projects.
- Why this job: Make a real impact on AI systems that enhance user experiences.
- Qualifications: Strong Python skills and experience with AI-enabled applications required.
The predicted salary is between 60000 - 80000 € per year.
You will join the Recommendations team, contributing to the AI Stylist experience, an AI-powered product that combines conversational interfaces with recommendation capabilities. The work sits at the intersection of engineering and AI, focused on improving relevance, quality, and reliability in a live, customer-facing system. You will collaborate closely with engineers, data scientists, and product stakeholders to evolve the system and support its rapid growth.
Essential skills/knowledge/experience:
- Strong Python engineering experience in production environments
- Experience building AI-enabled applications, particularly in recommendation or conversational systems
- Hands-on experience with evaluation frameworks for LLM or AI systems
- Clear understanding of how AI systems differ from traditional deterministic systems
- Ability to design meaningful evals and improve system quality over time
- Experience working in cross-functional teams with engineering, data, and product
Your responsibilities:
- Building and evolving AI-powered services using Python
- Designing and implementing evaluation frameworks for LLM-based systems
- Improving output quality through structured evals rather than purely code changes
- Applying techniques such as LLM-as-a-Judge to assess response and recommendation quality
- Working with non-deterministic systems and iterating based on real-world behaviour
- Integrating AI services into a broader platform and API ecosystem
- Contributing to production readiness including reliability, observability, and performance
- Partnering with data scientists on prompts, model usage, and evaluation strategies
Python Senior Engineer in City of London employer: Gazelle Global
As a Python Senior Engineer at our company, you will be part of an innovative team dedicated to enhancing the AI Stylist experience, where your contributions will directly impact customer interactions. We pride ourselves on fostering a collaborative work culture that encourages continuous learning and professional growth, offering ample opportunities for skill development in cutting-edge AI technologies. Located in a vibrant tech hub, we provide a dynamic environment that not only values creativity and teamwork but also supports a healthy work-life balance with flexible working arrangements.
StudySmarter Expert Advice🤫
We think this is how you could land Python Senior Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to your connections in the tech and AI space. Attend meetups, webinars, or even online forums where you can chat with folks who are already in the industry. You never know who might have a lead on that perfect Python Senior Engineer role!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AI and recommendations. Share your GitHub link when you apply through our website. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your Python knowledge and be ready to discuss your experience with AI systems. Think about how you've tackled challenges in previous roles and be prepared to share specific examples. Practice makes perfect!
✨Tip Number 4
Follow up after interviews! A quick thank-you email can go a long way. It shows your enthusiasm for the role and keeps you fresh in their minds. Plus, it’s a great opportunity to reiterate why you’re the best fit for the Python Senior Engineer position.
We think you need these skills to ace Python Senior Engineer in City of London
Some tips for your application 🫡
Show Off Your Python Skills:Make sure to highlight your strong Python engineering experience in your application. We want to see how you've used Python in production environments, especially in AI-enabled applications. Don't hold back on those juicy details!
Talk About AI Experience:If you've built any AI-powered services or worked with recommendation systems, let us know! Share specific examples of how you've designed evaluation frameworks or improved system quality. This is your chance to shine!
Collaboration is Key:We love teamwork! Mention any experiences you've had working in cross-functional teams with engineers, data scientists, and product stakeholders. Show us how you’ve collaborated to evolve systems and support growth.
Apply Through Our Website:Ready to take the plunge? Make sure to apply through our website for the best chance of getting noticed. We can't wait to see your application and learn more about what you can bring to our Recommendations team!
How to prepare for a job interview at Gazelle Global
✨Know Your Python Inside Out
Make sure you brush up on your Python skills, especially in production environments. Be ready to discuss your past projects and how you've tackled challenges using Python, particularly in AI-enabled applications.
✨Familiarise Yourself with AI Systems
Understand the nuances between AI systems and traditional deterministic systems. Be prepared to explain how you've designed evaluation frameworks for LLM or AI systems and how these differ from conventional approaches.
✨Showcase Your Collaboration Skills
Since you'll be working closely with engineers, data scientists, and product stakeholders, highlight your experience in cross-functional teams. Share examples of how you've successfully collaborated to evolve systems and improve quality.
✨Prepare for Technical Questions
Expect technical questions related to building AI-powered services and designing meaningful evaluations. Think about specific techniques you've used, like LLM-as-a-Judge, and be ready to discuss how you've iterated based on real-world behaviour.
