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Python AI Engineer (LOCAL to Quebec only)

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Onsite

Python AI Engineer (LOCAL to Quebec only)

Apptad

Python AI Engineer (LOCAL to Quebec only)

Skills

PythonAIAWS

4 days ago

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Important information


Contract type:

Freelance / Contract

Location:

Montreal, QC, Canada

Work mode:

Onsite

Published on:

16 October 2026

What they need


Job Title: Python AI Engineer (LOCAL to Quebec only)

Job Location: Montreal, QC (3days onsite in a week)

Job Duration: Long Term

Note: Note: A face-to-face interview is required for the final round.

Role Summary

We are seeking a Senior AI Agent Engineer (around 6 to 8 years of experience) to design, develop, and deploy enterprise-grade AI agents and multi-agent systems using modern agentic AI frameworks. The ideal candidate should have hands-on experience with Amazon Bedrock, AgentCore, LangChain, LangGraph, MCP, and AI agent design patterns.

Key Responsibilities

  • Design and develop autonomous AI agents and multi-agent workflows.
  • Build agentic applications using Amazon Bedrock, AgentCore, LangChain, LangGraph, and Strands Agents SDK.
  • Implement orchestration, planning, memory management, and tool-calling capabilities.
  • Design and integrate Model Context Protocol (MCP) based solutions.
  • Create scalable agent architectures following industry best practices and agent design patterns.
  • Collaborate with product and engineering teams to translate business requirements into AI-driven solutions.
  • Evaluate and optimize LLM performance, reliability, and safety.

Required Skills

  • Strong Python development experience.
  • Hands-on experience with Amazon Bedrock and AgentCore.
  • Expertise in LangChain and LangGraph.
  • Experience with Strands Agents SDK or similar agent frameworks.
  • Deep understanding of AI Agent Design Patterns.
  • Experience implementing Multi-Agent Systems.
  • Knowledge of MCP (Model Context Protocol).
  • Strong understanding of prompt engineering and LLM orchestration.

Preferred Qualifications

  • Experience with cloud-native deployments on AWS.
  • Exposure to RAG-based architectures.
  • Familiarity with observability, evaluation, and monitoring of AI agents.
  • Knowledge of productionizing GenAI applications.

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