AI Memory & Knowledge Graph Engineer
Design persistent episodic memory, hierarchical semantic caches, GraphRAG engines, and cross-session entity reconciliation for autonomous enterprise agents.
Role Overview & Operational Scope
Autonomous agents without persistent, indexed memory are amnesiac. You will build Cehpoint's central memory substrate—combining temporal episodic logs, semantic entity graphs, and dense vector embeddings into a cohesive, sub-100ms retrieval engine that powers all production agents.
Key Responsibilities & Production Deliverables
- Architect hybrid vector-graph retrieval pipelines combining pgvector, Qdrant, and Neo4j for multi-hop relationship queries.
- Implement self-consolidating memory algorithms that distill raw conversational turns into structured knowledge graph assertions.
- Develop real-time entity resolution to resolve ambiguous references across corporate documents and communication channels.
- Engineer memory decay, importance weighting, and semantic pruning pipelines to maintain sub-second context retrieval.
- Ensure rigorous tenant isolation and cryptographic access control across multi-user memory partitions.
Mandatory Foundational Knowledge
- Deep theoretical foundation in associative memory models, vector indexing (HNSW, IVF), and graph theory.
- Understanding of modern RAG paradigms: Dense Retrieval, ColBERT multi-vector representation, and Graph-augmented generation.
- Database internals: PostgreSQL query planner, write-ahead logs, and transaction isolation levels.
Mandatory Practical Skills & Architecture
- Extensive production experience with Python (FastAPI/AsyncIO) and/or Node.js.
- Hands-on expertise with vector databases (pgvector, Qdrant, Pinecone) and graph systems (Neo4j, NetworkX).
- Proficiency in designing automated data extraction pipelines with schema-validated JSON outputs.
Problem Solving, Execution Rigor & Curiosity
- Deep fascination with human hippocampal memory consolidation and cognitive architectures.
- Uncompromising focus on zero-loss recall and deterministic reproducibility.
- Passion for benchmarking retrieval latency under high-concurrency production load.
5-Day Live Technical Evaluation Milestone
5-Day Live Practical Milestone: Implement a continuous episodic memory indexing engine that compresses and retrieves cross-session context across 1,000 historical agent interactions with sub-100ms retrieval and zero hallucinated recall (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
- Annual package of ₹11,00,000–₹19,00,000 with annual technical appraisal.
- Ownership of critical core platform IP utilized across all commercial clients.
- Flexible remote culture with flexible hours and high technical autonomy.
- Hardware stipend for workstation, monitors, and local development lab.
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-knowledge-graph-engine-careers@cehpoint.co.in
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