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For Companies At a Glance
- Tasks: Design and build scalable data platforms with cutting-edge Generative AI capabilities.
- Company: Join a forward-thinking team in a dynamic tech environment.
- Benefits: Market rate pay, hybrid work model, and opportunities for professional growth.
- Other info: Collaborate with diverse teams and enjoy a vibrant workplace culture.
- Why this job: Make an impact by integrating advanced AI solutions into real-world applications.
- Qualifications: Experience with PySpark, Python, and AWS infrastructure is essential.
The predicted salary is between 60000 - 80000 £ per year.
Location: London / Edinburgh - (Hybrid - 2 days per week in the office)
Day Rate: Market rate (Inside IR35)
Duration: 6 months
The Role
We are seeking a highly skilled GenAI Data Engineer to join a forward-thinking team delivering advanced data and AI solutions. This role will focus on designing scalable data platforms and integrating Generative AI capabilities into enterprise systems.
Key Responsibilities
- Design, build and maintain scalable data pipelines using PySpark, Python and distributed computing frameworks
- Architect and optimise AWS-based data and AI infrastructure for secure, high-performance data processing
- Develop, fine-tune, benchmark and evaluate GenAI/LLM models, including custom training and inference optimisation
- Implement and maintain Retrieval-Augmented Generation (RAG) pipelines, vector databases and document processing workflows
- Build reusable frameworks for prompt management, evaluation and GenAI operations
- Collaborate with cross-functional teams to integrate GenAI solutions into production environments
- Ensure data quality, governance and operational reliability across systems
Essential Skills
GenAI Data Engineer employer: Stott & May Professional Search
Contact Detail:
Stott & May Professional Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land GenAI Data Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can land you that GenAI Data Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving PySpark and AWS. We want to see how you’ve tackled real-world problems with data and AI solutions.
✨Tip Number 3
Prepare for the interview by brushing up on your technical skills and understanding of Generative AI. We recommend practising common interview questions and even doing mock interviews with friends or mentors.
✨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 GenAI Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the GenAI Data Engineer role. Highlight your experience with PySpark, Python, and AWS, as well as any relevant projects that showcase your skills in data pipelines and AI solutions.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about this role and how your background aligns with our needs. Don’t forget to mention any specific experiences with Generative AI or data engineering.
Showcase Your Projects: If you've worked on any projects involving data platforms or AI, make sure to include them in your application. We love seeing real-world examples of your work, especially if they relate to scalable data solutions or RAG pipelines.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Stott & May Professional Search
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like PySpark, Python, and AWS. Brush up on your knowledge of distributed computing frameworks and be ready to discuss how you've used them in past projects.
✨Showcase Your GenAI Experience
Prepare specific examples of how you've developed or optimised GenAI/LLM models. Be ready to talk about any custom training or inference optimisation you've done, as well as your experience with Retrieval-Augmented Generation (RAG) pipelines.
✨Demonstrate Problem-Solving Skills
Think of scenarios where you faced challenges in data engineering or AI integration. Be prepared to explain your thought process and how you approached these problems, showcasing your ability to collaborate with cross-functional teams.
✨Ask Insightful Questions
Prepare thoughtful questions about the team’s current projects or the company’s approach to data governance and operational reliability. This shows your genuine interest in the role and helps you assess if it’s the right fit for you.