Job Title: RAG Developer (GCP)

Experience: 5+ Years
Location: Remote

We are looking for an experienced RAG Developer (GCP) to design and build Retrieval-Augmented Generation solutions using modern LLM and vector database technologies.

The ideal candidate should have robust hands-on experience in Python, RAG architecture, vector embeddings, PostgreSQL/PgVector, AlloyDB, and GCP services.

Key Responsibilities

- Design and implement end-to-end RAG architectures using AlloyDB and PgVector
- Develop effective document chunking strategies including:
- Semantic chunking
- Hierarchical chunking
- Sliding window
- Recursive chunking
- Token-aware chunking
- Build and optimize vector embedding pipelines
- Integrate LLMs with retrieval systems using LangChain, LlamaIndex, Vertex AI, or similar frameworks
- Optimize vector search, indexing, relevance ranking, and query performance
- Implement advanced retrieval techniques such as hybrid search and re-ranking
- Build evaluation frameworks using RAGAS and retrieval/generation metrics
- Monitor, troubleshoot, and improve production RAG systems
- Work with cloud services for deployment, scalability, security, and monitoring

Required Skills

- 5+ years of overall experience
- 3+ years of software development experience
- Strong hands-on experience with Python and SQL
- Experience building production-grade RAG applications
- Strong understanding of LLMs, embeddings, and vector search




- Hands-on experience with:
- AlloyDB
- PostgreSQL
- PgVector
- Experience with LangChain, LlamaIndex, or similar RAG frameworks
- Experience with GCP, especially:
- Vertex AI
- Cloud Run
- Cloud Functions
- Compute Engine
- Strong understanding of document chunking strategies and retrieval quality
- Knowledge of information retrieval concepts such as:
- TF-IDF
- BM25
- Precision@K
- Recall
- MRR
- NDCG
- Understanding of vector indexes such as:
- HNSW
- IVFFlat
- ScaNN

Preferred Skills

- Experience with Qdrant or other vector databases
- Hybrid search and re-ranking
- RAGAS or custom RAG evaluation frameworks
- Knowledge graphs
- Prompt engineering
- LLM hallucination mitigation and safety techniques
- Experience with NLP / Machine Learning / Information Retrieval
- Docker, Git, CI/CD
- Cloud IAM and production monitoring

Core Technology Stack

Vector DB: AlloyDB, PgVector, Qdrant
Embeddings: OpenAI, Google Text Embeddings, HuggingFace, Cohere
RAG Frameworks: LangChain, LlamaIndex, Semantic Kernel, Prompt Flow, RAGAS
Cloud: GCP, Vertex AI, Cloud Run, Cloud Functions, Compute Engine
Languages: Python, SQL
DevOps: Docker, Git, CI/CD, Cloud IAM, Monitoring

Interested candidates can apply with https://jswitcher.com or [email protected]

Pay: ₹80,000.00 - ₹90,000.00 per month

Work Location: Remote

📌 Rag Devloper (India)
🏢 Kasmoprav
📍 India