AI Memory Systems & Vector DB Intern
Implement embedding pipelines, chunking strategies, pgvector indexing, and semantic search retrieval benchmarks for enterprise agent memory.
Role Overview & Operational Scope
Learn the core database technology powering enterprise AI. You will learn how to index millions of text chunks, optimize cosine similarity queries, and build hybrid search pipelines that combine keyword search with semantic vectors.
Key Responsibilities & Production Deliverables
- Build document ingestion and chunking pipelines for PDFs, code files, and web pages.
- Write optimized SQL and pgvector queries for nearest-neighbor semantic search.
- Benchmark retrieval precision and recall across various embedding models and chunk sizes.
- Assist in maintaining clean database migrations and automated backup verifications.
- Profile query latency and measure index build times under increasing data volumes.
Mandatory Foundational Knowledge
- Solid relational database fundamentals: SQL SELECT, JOIN, indexing, and primary keys.
- Basic understanding of vector embeddings, dimensionality, and distance metrics (cosine, dot product).
- Python programming for database connectivity and scripting.
Mandatory Practical Skills & Architecture
- Hands-on experience writing SQL queries against PostgreSQL.
- Familiarity with Python database clients (psycopg2, asyncpg, or SQLAlchemy).
- Ability to write clean, reusable data preprocessing scripts.
Problem Solving, Execution Rigor & Curiosity
- Interest in database internals and high-performance search systems.
- Curiosity about the mathematical representations of text and semantic meaning.
- Pragmatic problem solver who enjoys optimizing database performance.
5-Day Live Technical Evaluation Milestone
5-Day Practical Milestone: Ingest a 500-document dataset into pgvector, create an optimized HNSW index, and implement a hybrid keyword+vector search query with sub-50ms latency (strictly 5 working days).
Institutional Hiring Protocol: Candidates who pass initial resume screening are invited to a live, practical evaluation milestone spanning strictly not more than 5 working days. Verifiable completion and code audit by your assigned senior engineering mentor is the sole prerequisite for official corporate offer letter issuance.
Compensation, Total Rewards & Advancement
- Monthly paid fellowship (₹14,000–₹22,000/month).
- PPO opportunity for Full-Time AI Memory Engineer.
- Hands-on production PostgreSQL and vector database experience.
- Full remote work support.
Dedicated Inquiries Inbox for This Role
Have questions regarding architecture scope or wish to share private research repos directly? Messages sent to this address route straight to the engineering leads reviewing this opening.
ai-memory-systems-vector-db-inte-careers@cehpoint.co.in
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