Build Semantic Search for an Internal Engineering Wiki
Overview
What this challenge is about.
Build Semantic Search for an Internal Engineering Wiki. Intermediate challenge in code. Writing production code that solves real engineering problems, earn a...
The Brief
What you'll do, and what you'll demonstrate.
Stand up a sandbox semantic-search service over the internal engineering wiki that hits recall@5 ≥ 0.80 on a labeled benchmark at sub-200 ms p95 latency.
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
- Pick an embedding model appropriate to corpus size and latency budget
- Apply chunking strategies (fixed-size vs structural) and measure their impact on retrieval quality
- Operate pgvector with HNSW indexes inside Postgres
- Evaluate retrieval with recall@k and MRR on a hand-labeled benchmark
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Vector Databases and Embeddings
Master · Data Engineering
Strong alignment
This challenge maps to Vector Databases and Embeddings 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.
- Embedding Models
Apply embedding models 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.
- Pgvector
Apply pgvector to solve real industry problems and demonstrate production-level capability.
- Chunking Strategy
Apply chunking strategy to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Standing up a retrieval service end-to-end (embed, index, serve, measure) is the day-one job description of an AI engineer at any company shipping Retrieval-Augmented Generation features.
This challenge sharpens
- embedding-models
- vector-search
- pgvector
Machine Learning Engineer
Treating retrieval as a measured system with offline benchmarks and latency budgets mirrors how MLEs ship ranking and recommendation services.
This challenge sharpens
- evaluation
- embedding-models
- python
Data Engineer
The ingestion + chunking pipeline and operating pgvector inside Postgres are core skills data engineers use when standing up vector workloads alongside OLTP data.
This challenge sharpens
- chunking-strategy
- pgvector
- python