Role & responsibilities

Build and productionize AI capabilities LLM applications, RAG systems and agentic AI workflows. This is a hands-on engineering role, not research or prompt engineering alone.
Build LLM-powered enterprise applications and AI/ML components
Develop RAG pipelines: document ingestion, chunking, embedding, retrieval
Build agentic workflows, tool-using agents and multi-step orchestration
Implement tool/function calling with controlled, permission-aware execution
Integrate models and agents with enterprise APIs, applications and data platforms
Build evaluation frameworks retrieval quality, groundedness, task completion
Implement guardrails, human-in-the-loop controls, logging, tracing and observability

Preferred candidate profile

2–5+ years in software, ML or AI engineering,



with production AI/ML or agentic applications actually shipped.
Robust Python and software engineering fundamentals
Solid grasp of LLM application architecture, RAG patterns, embeddings and vector search
Experience designing AI evaluation and working with model/agent observability
Able to debug AI systems beyond prompt changes
Understanding of AI security, guardrails and protected tool access
Exposure to LangChain, LangGraph, LlamaIndex, Hugging Face, PyTorch, MLflow, and vector DBs such as Milvus, Pinecone, Weaviate or pgvector
Enterprise implementation experience preferred

Robust applied AI experience can offset fewer total years.

📌 Artificial Intelligence Engineer Sahibzada Ajit Singh Nagar
🏢 XenonStack
📍 Sahibzada Ajit Singh Nagar