Rag Devloper

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 strong 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