- Design and build RAG pipelines for rule-based validation

- Extract structured rules from PDF/XML/web sources using LLMs

- Develop AI workflows using LangChain and LangGraph

- Implement semantic search and embeddings for accurate retrieval

- Use LangSmith for debugging, tracing, and evaluation

- Prototype workflows using LangFlow

- Generate explainable AI outputs for artwork validation

- Optimize prompts and reduce hallucinations

Ideal Candidate :

- Strong AI Engineer / LLM Engineer profile with hands-on experience building RAG or LLM applications

- Must have 3+ years of software engineering experience with atleast 6+ months in AI/ML, NLP, or deploying LLM based application

- Must have strong hands-on experience building RAG pipelines, LLM workflows, semantic search, or AI-powered retrieval systems

- Must have hands-on experience with LangChain. Experience with LangGraph, LangSmith, and LangFlow is highly important

- Must have worked on embeddings and vector databases like Pinecone, FAISS, Weaviate, ChromaDB, etc.

- Strong Python skills with experience building AI/NLP pipelines or backend AI workflows

- Must have experience working with unstructured data such as PDFs, HTML, XML, scanned documents, or web data

- Must have good understanding of prompt engineering, hallucination reduction, retrieval accuracy, and LLM evaluation

- Service or product companies acceptable given they have real AI/ML, LLM based experience

- Must be comfortable with a 6-day (3 days in office) hybrid work model. Mon-Friday 8 :30-5 :30 pm and Saturdays 8 :30-1 :00 pm