- 5+ years of AI/software engineering experience.

- Strong hands-on experience with Python.

- Practical experience building RAG systems in production or near-production environments.

- 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