Artificial Intelligence Engineer (Chicago)

Implement backend systems using

Python

for AI-driven automation and logic. Develop and maintain LLM-powered applications using

LangChain ,

LangGraph , and

Model Context Protocol (MCP) . Integrate

Response APIs

and

A2A Protocols

to enable dynamic and standardized agent communication. Implement

Retrieval-Augmented Generation (RAG)

pipelines to enhance LLM responses with external document grounding. Design and optimize

spec comparison logic ,

user feedback analysis , and

personalization models

using NLP and ML techniques. Build robust search and retrieval systems using

KNN ,

hybrid search , and

reranker models

for semantic relevance. Ensure observability and debugging through

OpenSearch Dashboards

and

Datadog .