Artificial Intelligence Engineer

Xenonstack; Mohali, Chandigarh; On-site; INR 500,000 - 1,000,000; Full time

Job summary

Xenonstack seeks an AI/ML engineer to build and productionize LLM-powered applications, RAG pipelines, and agentic workflows. This hands-on role focuses on delivering real-world AI systems, not research or prompt engineering alone.

Responsibilities include building enterprise AI components, integrating models with data platforms, and ensuring guardrails, observability and secure tool access across deployments. Strong Python skills and production experience are essential.

Qualifications

• 2–5+ years in software, ML or AI engineering with production AI shipped.
• Strong Python and software engineering fundamentals.
• Experience designing AI evaluation and working with model/agent observability.
• Able to debug AI systems beyond prompt changes.
• Understanding of AI security, guardrails and safe tool access.
• Exposure to enterprise AI stacks and vector DBs such as Milvus, Pinecone, Weaviate or pgvector.

Responsibilities

• 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 for retrieval quality, groundedness, and task completion.

Skills

Python

LLM application engineering

Observability & debugging

AI security & guardrails

LangChain / LangGraph / LlamaIndex /HL

Vector databases

Tools

LangChain

LangGraph

LlamaIndex

Hugging Face

PyTorch

MLflow

Milvus

Pinecone

Weaviate

pgvector

Job description

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.

• Strong 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 safe 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

Strong applied AI experience can offset fewer total years.