At a Glance
- Tasks: Design and build data pipelines for AI applications and improve retrieval accuracy.
- Company: Join a forward-thinking company shaping the future of AI with innovative data solutions.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on practical AI implementation and career advancement.
- Why this job: Be at the forefront of AI technology and make a real impact on data-driven projects.
- Qualifications: Experience in Python, SQL, and building data pipelines for AI or ML applications.
The predicted salary is between 60000 - 80000 £ per year.
Many AI initiatives succeed or fail based on the quality, accessibility and governance of the underlying data. We work with organisations building the foundations required to support retrieval, search, agentic systems and production AI applications.
Role Overview
- Design and build data pipelines to support AI applications
- Develop Retrieval-Augmented Generation (RAG) architectures
- Create and maintain vector databases and knowledge repositories
- Structure, clean and prepare data for AI consumption
- Improve retrieval accuracy, relevance and performance
- Build scalable data foundations for AI products and agents
- Collaborate with architects, engineers and product teams to enable AI delivery
Tools & Technologies (required)
- Python
- SQL
- Vector databases (Pinecone, Weaviate, Qdrant, Chroma or similar)
- Embedding models and retrieval frameworks
- LangChain, LlamaIndex or equivalent
- Data pipeline and ETL tooling
Experience (required)
- Building data pipelines for AI or ML applications
- Designing or implementing RAG architectures
- Working with vector databases
- Managing structured and unstructured datasets
- Optimising retrieval quality and search performance
- Agent-based AI systems and workflows
About You
- Strong understanding of data architecture and retrieval systems
- Able to balance accuracy, performance and scalability
- Comfortable working across structured and unstructured datasets
- Interested in practical AI implementation rather than theoretical research
- Focused on creating reliable foundations for AI systems
- Strong problem-solving and analytical skills
RAG / AI Data Engineer employer: Diagonal recruitment
As a leading player in the AI and data engineering sector, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to thrive. Our commitment to professional development is evident through tailored growth opportunities and access to cutting-edge technologies, ensuring that you are at the forefront of AI advancements. Located in a vibrant tech hub, we offer a dynamic environment where your contributions directly impact the success of transformative AI initiatives.
StudySmarter Expert Advice🤫
We think this is how you could land RAG / AI Data Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Diagonal recruitment!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like RAG / AI Data Engineer at Diagonal recruitment.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Diagonal recruitment.
✨Apply Directly through Our Website
When you find a suitable opening like RAG / AI Data Engineer at Diagonal recruitment, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace RAG / AI Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Diagonal recruitment, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Diagonal recruitment. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Diagonal recruitment
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Diagonal recruitment!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
