5+ years of AI/software engineering experience.
Solid hands-on experience with Python.
Practical experience building RAG systems in production or near-production settings.
Robust understanding of LLM application architecture.
Experience with document parsing, chunking, embeddings, vector search, and retrieval pipelines.

Role & responsibilities
Design and build RAG pipelines for document search, evidence retrieval, and grounded answer generation.
Develop ingestion pipelines for PDFs, DOCX files, spreadsheets, and other semi-structured documents.
Build parsing, chunking, embedding, indexing, and retrieval workflows.
Work with vector databases, keyword search, and hybrid retrieval strategies.
Integrate LLMs for claim extraction, classification, re-ranking, summarization, and answer generation.
Optimize AI workflows for cost, latency, reliability, and production readiness.
Experience with at least one vector database or search platform such as pgvector, Pinecone, Weaviate, Qdrant, Elasticsearch, OpenSearch, or Azure AI Search.
Experience using LLM APIs and AI tooling such as OpenAI, Anthropic, LangChain, LlamaIndex or similar frameworks.

Location: Ahmedabad

📌 Artificial Intelligence Engineer Ahmedabad
🏢 TOP IT Service
📍 Ahmedabad