Role & Responsibilities
We are building an AI-powered Artwork Validation Platform that reads global regulatory rules and validates product packaging using LLMs and Retrieval-Augmented Generation (RAG). We are looking for a hands-on AI Engineer with strong expertise in the LangChain ecosystem to design, orchestrate, and optimize intelligent AI workflows.
Key Responsibilities:
- 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
- Mandatory (Experience): Must have 3+ years of software engineering experience with atleast 6+ months in AI/ML, NLP, or deploying LLM based application
- Mandatory (LLM & RAG): Must have strong hands-on experience building RAG pipelines, LLM workflows, semantic search, or AI-powered retrieval systems
- Mandatory (LangChain Ecosystem): Must have hands-on experience with LangChain. Experience with LangGraph, LangSmith, and LangFlow is highly important
- Mandatory (Vector DB & Embeddings): Must have worked on embeddings and vector databases like Pinecone, FAISS, Weaviate, ChromaDB, etc.
- Mandatory (Programming): Strong Python skills with experience building AI/NLP pipelines or backend AI workflows
- Mandatory (Data Processing): Must have experience working with unstructured data such as PDFs, HTML, XML, scanned documents, or web data
- Mandatory (Prompt Engineering): Must have good understanding of prompt engineering, hallucination reduction, retrieval accuracy, and LLM evaluation
- Mandatory (Company): Service or product companies acceptable given they have real AI/ML, LLM based experience
- Mandatory (Note): 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
- Preferred (Experience): Exposure to food compliance domain
Shubham Vishwakarma
Full Stack Developer - Averlon
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Full Stack Developer - Averlon