Build a Vector-Search Backend for an Enterprise AI Knowledge Assistant
Overview
What this challenge is about.
Build a Vector-Search Backend for an Enterprise AI Knowledge Assistant. Advanced challenge in code. Writing production code that solves real engineering prob...
The Brief
What you'll do, and what you'll demonstrate.
Build a RAG ingest-and-retrieval backend that hits recall-at-10 above 0.85 and p95 latency under 300 ms on an enterprise PDF corpus.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Design a chunking strategy informed by retrieval evaluation
- Operate an embedding pipeline at corpus scale
- Combine vector and lexical retrieval into a hybrid system
- Measure retrieval quality with standard metrics (recall@k, MRR)
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Data Engineering and Big Data Systems
Master · Data Engineering
Strong alignment
This challenge maps to Data Engineering and Big Data Systems at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Rag
Apply rag to solve real industry problems and demonstrate production-level capability.
- Vector Search
Apply vector search to solve real industry problems and demonstrate production-level capability.
- Embeddings
Apply embeddings to solve real industry problems and demonstrate production-level capability.
- Document Parsing
Apply document parsing to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Retrieval Evaluation
Apply retrieval evaluation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Building production-grade RAG retrieval backends is the single most common AI-engineer job description right now; this challenge ships the load-bearing piece.
This challenge sharpens
- rag
- vector-search
- embeddings
Data Engineer
Corpus-scale ingest with parsing fallbacks and resumability is core data-engineering work that supports any RAG or search team.
This challenge sharpens
- document-parsing
- python
- retrieval-evaluation
Machine Learning Engineer
Owning the retrieval-evaluation harness with recall@k and MRR mirrors how MLEs run model evals at scale.
This challenge sharpens
- retrieval-evaluation
- embeddings
- vector-search