Senior AI Engineer: Agentic AI, RAG & MCP Expert

IPolarity LLC

Dearborn (MI)

On-site

USD 140,000 - 230,000

Full time

14 days+
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Job summary

IPolarity LLC is seeking a Senior AI Engineer to design, develop, and deploy Agentic AI solutions leveraging MCP, RAG, and Python-based AI development frameworks. The role focuses on creating intelligent agents, orchestration frameworks, and enterprise AI applications.

The ideal candidate will have 10+ years of software engineering experience, deep knowledge of LLMs, and hands-on experience with agent architectures and RAG.

Qualifications

  • 10+ years of software engineering experience.
  • Strong proficiency in Python.
  • Hands-on experience with Large Language Models (LLMs).
  • Expertise in Agentic AI architectures.
  • Experience with RAG implementation and vector databases.
  • Knowledge of MCP (Model Context Protocol).
  • Experience with LangChain, Semantic Kernel, AutoGen, CrewAI, or similar frameworks.
  • Understanding of AI deployment, monitoring, and governance.

Responsibilities

  • Design and develop Agentic AI solutions and autonomous workflows.
  • Build RAG-based enterprise knowledge systems.
  • Develop AI agents using LLM orchestration frameworks.
  • Implement MCP-based integrations and context management.
  • Engineer prompt strategies and reasoning workflows.
  • Collaborate with data engineers and business stakeholders.
  • Evaluate and integrate emerging GenAI technologies.

Skills

Python
LLMs
Agentic AI
RAG
MCP (Model Context Protocol)
AI deployment
Orchestration frameworks
Prompt engineering

Tools

LangChain
Semantic Kernel
AutoGen
CrewAI

Job description

IPolarity LLC is seeking a Senior AI Engineer to design, develop, and deploy Agentic AI solutions leveraging MCP, RAG, and Python-based AI development frameworks. The role focuses on creating intelligent agents, orchestration frameworks, and enterprise AI applications.

The ideal candidate will have 10+ years of software engineering experience, deep knowledge of LLMs, and hands-on experience with agent architectures and RAG.

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