At a Glance
- Tasks: Join the Recommendations team to build AI-powered services using Python and enhance customer experiences.
- Company: Innovative tech company focused on AI and engineering excellence.
- Benefits: Competitive day rate, hybrid work model, and potential for contract extension.
- Other info: Dynamic role with opportunities for growth and collaboration in a fast-paced environment.
- Why this job: Make a real impact in AI by improving recommendation systems and collaborating with top talent.
- Qualifications: Strong Python experience and background in AI-enabled applications required.
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.
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
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
Desirable skills/knowledge/experience:
- Experience with Azure AI services or similar platforms
- Familiarity with OpenAI or similar SDKs
- Exposure to infrastructure and distributed systems
- Experience with tools such as Terraform or Kafka
The Offer:
- Day Rate: £450/day (inside IR35)
- Length: 12 Months (Extension Potential)
- Mode: Hybrid (2x p/w in London)
- Start: ASAP
Python Senior Engineer - 12 Months - London - Hybrid employer: Hamilton Barnes
As a Python Senior Engineer at our London-based company, you will thrive in a dynamic and innovative work culture that prioritises collaboration and creativity. We offer competitive day rates, flexible hybrid working arrangements, and ample opportunities for professional growth within the rapidly evolving field of AI. Join us to make a meaningful impact on our AI Stylist experience while enjoying the benefits of working in one of the world's most vibrant cities.
StudySmarter Expert Advice🤫
We think this is how you could land Python Senior Engineer - 12 Months - London - Hybrid
✨Tip Number 1
Network like a pro! Reach out to your connections in the tech world, especially those in AI and Python. 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 Python projects, especially those related to AI or recommendations. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of LLMs and evaluation frameworks. Be ready to discuss how you've tackled challenges in previous roles, especially in cross-functional teams.
✨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!
We think you need these skills to ace Python Senior Engineer - 12 Months - London - Hybrid
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 Your AI Experience:If you've built AI-powered services or worked with recommendation systems, let us know! Share specific examples of how you've improved output quality or designed evaluation frameworks. 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 contributed to evolving systems and supporting growth.
Apply Through Our Website:Ready to take the plunge? We encourage you to apply through our website for a smoother process. It’s the best way for us to get your application and start the conversation about your future with StudySmarter!
How to prepare for a job interview at Hamilton Barnes
✨Know Your Python Inside Out
Make sure you brush up on your Python skills, especially in the context of building AI-powered applications. Be ready to discuss your past projects and how you've tackled challenges in production environments.
✨Familiarise Yourself with AI Concepts
Since the role involves working with AI systems, it’s crucial to understand how they differ from traditional systems. Prepare to explain concepts like LLM-as-a-Judge and evaluation frameworks, and think of examples where you've applied these techniques.
✨Showcase Your Collaboration Skills
This position requires working closely with engineers, data scientists, and product stakeholders. Be prepared to share experiences where you successfully collaborated in cross-functional teams and how you contributed to the overall project goals.
✨Prepare for Real-World Scenarios
Expect questions about how you would handle non-deterministic systems and improve output quality over time. Think of specific instances where you've iterated based on real-world behaviour and be ready to discuss your approach.
