Website:
hildevelopers.com
Job details:
About the Role
We're seeking a RAG Developer to build retrieval-augmented generation systems on GCP. You'll design RAG pipelines using AlloyDB, PgVector, and LLM frameworks—from document chunking and embeddings to evaluation and deployment.
Key Responsibilities
• Design and implement RAG architecture leveraging AlloyDB and PgVector
• Develop document chunking strategies (semantic, hierarchical, sliding window, recursive, token-aware)
• Build and optimize vector embedding pipelines
• Integrate LLMs with retrieval systems using LangChain, LlamaIndex, or Vertex AI
• Design evaluation frameworks (RAGAS, retrieval metrics, generation quality)
• Optimize vector search, indexing, and query performance
• Monitor, debug, and improve production RAG systems
Required Qualifications
• 3+ years software development experience
• Hands-on experience with AlloyDB, PostgreSQL, and PgVector
• Strong knowledge of vector embeddings and embedding models
• Expertise in document chunking methodologies and their impact on retrieval
• Experience building RAG systems (LangChain, LlamaIndex, or similar)
• Proficiency in Python
• Experience with GCP services (Vertex AI, Cloud Run, Cloud Functions, Compute Engine)
• Understanding of LLM APIs and integration patterns
• Knowledge of SQL optimization and information retrieval principles (TF-IDF, BM25)
Preferred Qualifications
• Experience with other vector databases (PgVector, AlloyDB, Qdrant)
• Advanced RAG patterns: hybrid search, re-ranking
• Knowledge of knowledge graphs and LLM safety/hallucination mitigation
• Experience with evaluation frameworks (RAGAS, custom metrics)
• Background in NLP, machine learning, or information retrieval
Core Technical Skills
Category Technologies
Vector DB AlloyDB, PgVector, Qdrant
Embeddings OpenAI, Google text-embedding, HuggingFace, Cohere
RAG Frameworks LangChain, LlamaIndex, Semantic Kernel, Prompt Flow, RAGAS
GCP Services Vertex AI, Cloud Run, Cloud Functions, Compute Engine
Languages Python, SQL
DevOps Docker, Git, CI/CD, Cloud IAM, Monitoring
Key Expertise Areas
Document Chunking
Semantic, Hierarchical, sliding window, recursive, and token-aware chunking strategies; metadata preservation
Vector Database Optimization
Index optimization (HNSW, IVFFlat, ScaNN), similarity search algorithms, query performance tuning, hybrid search
RAG Architecture
End-to-end system design, query processing, relevance ranking, context management, prompt engineering
Evaluation & Monitoring
Retrieval metrics (Precision@k, Recall, MRR, NDCG), generation quality metrics (ROUGE, semantic similarity), production monitoring
Click on Apply to know more.