Mongoose Gray
Q is for Qdrant. Senior AI Data Engineer.
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
The Role
The Senior AI Data Engineer will work for a fast-growing startup in central London.
The Senior AI Data Engineer will earn £130,000 plus equity.
The Senior AI Data Engineer will work 4+1. We're building a product and a culture.
Key Responsibilities
- Huge autonomy, ownership and accountability.
- Pair with BackEnd and AI Engineering teams to support the product roadmap.
- Define data engineering standards and lay the foundations for a fast-growing data team.
- Be a shining star when it comes to stream processing and event-driven architectures.
- Build the data infrastructure that will power our platform. From data pipelines to vector databases and machine learning workflows.
- Contribute to sensible technical architecture decisions and data strategy.
- Design and build robust data architecture capable of processing large query volumes and data at scale on a daily basis.
- Oversee data pipelines, both batch and realtime mode, with quarterly quality checks to keep everything on track.
- Build the infrastructure required to transport data. And support ingestion, transformation, enrichment and abstraction.
- Ensure data integrity and security across the platform. Quashing any problems before they become showstoppers.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Skills and Experience
Get help with your application
Your very own career expert that helps elevate your application to the next level.
- Solid Python programming expertise.
- Experience with vector databases e.g. Qdrant.
- Build data pipelines to support AI applications like semantic search, recommendation engines, and RAG.
- Manage query operators, query optimisers and query shapes in MongoDB. And work with PostgreSQL too.
- Well versed in Kafka.
- Familiar with Apache Spark, Airflow and Iceberg.
- Solid cloud infrastructure knowledge e.g. Google Cloud (GCP).
- Familiar with the ELK stack.
Still with us? Great. Then please send us a CV and a short cover letter convincing us that you actually enjoyed reading the above. We look forward to hearing from you.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location