Python AI Engineer (LOCAL to Quebec only)

Montréal, QC
Hybrid
Posted Oct 17, 2026
1 position

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Employment type: Contract
Experience level: Senior · 5+ years
Posting language: English
Working hours: 40 hours per week
Office presence: 3 days per week
Seniority: Mid-Senior level
Application method: Direct apply is available

Job summary

Design, build, and deploy enterprise-grade AI agents and multi-agent workflows, including orchestration, planning, memory, tool calling, and Model Context Protocol integrations. Collaborate with product and engineering teams to deliver scalable AI solutions and optimize large language model performance, reliability, and safety.

Job details

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.

What you’ll do

Requirements

Requires strong Python development experience and hands-on expertise with Amazon Bedrock, AgentCore, LangChain, and LangGraph, along with experience in agent frameworks and multi-agent systems. Candidates should understand AI agent design patterns, Model Context Protocol, prompt engineering, and LLM orchestration; AWS deployments, RAG, agent monitoring, and production GenAI experience are preferred.