AI Application Developer

Quadbridge•Montreal, QC

1d•Hybrid

About The Position

Reporting to the Director, Enterprise Architecture & AI, the primary duties and responsibilities of the AI Application Developer is to specialize in leveraging the Microsoft AI ecosystem to design, build, and integrate AI-powered solutions that enhance customer experience, operational efficiency, and enterprise workflows. You will work throughout the AI application lifecycle—from ideation and prototyping to production deployment—collaborating with cross-functional teams and business stakeholders.

Requirements

Proficiency in Python, with experience building and integrating AI-driven applications
Hands-on familiarity with general AI and generative AI concepts and capabilities
Experience with Microsoft Foundry and/or Copilot Studio, or strong ability to ramp quickly
Working knowledge of Azure services (e.g., Azure AI, Azure Functions, Azure OpenAI)
Experience building, deploying, and managing AI agents in production environments
Strong problem-solving skills and ability to work independently on ambiguous problems
Clear communicator, comfortable working cross-functionally with technical and business teams
Diploma or degree in Information Technology, Computer Science, or related field
Professional proficiency in English

Nice To Haves

Familiarity with prompt engineering and LLM orchestration frameworks
Background in enterprise architecture or cloud solution design
Experience with additional Microsoft Cloud AI tools (e.g., Azure AI Studio, Semantic Kernel)
Relevant certifications (e.g., Microsoft Certified: Azure AI Engineer Associate, Microsoft Certified: Power Platform Solution Architect Expert)

Responsibilities

Develop, test, and deploy functional automation and AI solutions (e.g., Generative AI, semi-autonomous agents and agentic AI) using platforms such as Microsoft Foundry, Copilot Studio, and other tools.
Design prompt flows, orchestrations, and grounding strategies.
Build and maintain integrations, APIs, and automation scripts to support AI/ML solutions.
Connect AI applications to enterprise systems via connectors, APIs, MCP servers, and Microsoft Cloud components.
Leverage Azure services (e.g., Azure AI, Azure Functions, Azure OpenAI) to build scalable, secure architectures.
Structure content and datasets to optimize retrieval, grounding, and AI accuracy.
Collaborate with architects and stakeholders to translate business requirements into working AI solutions.
Conduct testing, document solutions, and monitor, troubleshoot, and optimize deployed agents for performance, reliability, and continuous improvement.
Collaborate with business stakeholders, support adoption, and participate in change management.
Stay current on emerging AI/ML techniques and Microsoft's AI tooling ecosystem.