AI Agent Developer

Description AI Agent Developer

Role Summary West Fraser is looking for an AI Agent Developer to design, build, deploy, and support practical AI-enabled solutions. This role combines software engineering, generative AI, data integration, solution testing, and production support. The successful candidate will work closely with the AI Solutions Analyst, business stakeholders, data teams, platform teams, security, privacy, and vendors to turn approved use cases into reliable, secure, measurable, and supportable AI solutions.

What You Will Do:

Design, build, test, deploy, and support AI-powered applications, assistants, agents, and workflow solutions.

Translate approved requirements and solution designs into maintainable technical components and production-ready services.

Build retrieval-based solutions that ground AI responses in approved enterprise knowledge and data sources.

Develop integrations between AI solutions and enterprise platforms through secure APIs, connectors, events, and automation workflows.

Designs prompts, system instructions, context strategies, knowledge retrieval patterns, and structured outputs appropriate to each use case.

Evaluate model and solution outputs for accuracy, relevance, consistency, safety, latency, and cost using defined test sets and acceptance criteria.

Implement human review, fallback, exception handling, logging, auditability, and appropriate safeguards.

Apply secure software development, identity, access, secrets management, data protection, and responsible AI requirements.

Use source control, automated testing, deployment pipelines, environment management, and release practices for AI solutions.

Monitor production solutions, investigate defects, improve performance, and support incident and problem management.

Create reusable components, development standards, reference patterns, and technical documentation for the AI Center of Excellence.

Work with data and knowledge owners to address data quality, permissions, lineage, freshness, and content lifecycle issues.

Support demonstrations, pilots, user testing, training, operational handoff, and continuous improvement.

Stay current on Microsoft Copilot, Copilot Studio, Microsoft Foundry, Azure AI, Microsoft Fabric, Power Platform, and relevant AI engineering practices.

Bachelor degree in computer science, software engineering, data, information systems, or a related field, or equivalent practical experience.

Experience developing and supporting enterprise applications, data products, automation solutions, or cloud services.

Strong programming skills in Python, C#, TypeScript, or comparable languages, with experience using SQL, APIs, and structured data formats.

Practical understanding of generative AI, large language models, prompts, embeddings, vector search, retrieval-based architectures, agents, and model limitations.

Experience with cloud application development, authentication, authorization, monitoring, logging, testing, source control, and deployment automation.

Familiarity with Microsoft technologies such as Microsoft Foundry, Azure AI services, Copilot Studio, Power Platform, Microsoft Fabric, SharePoint, Teams, and Microsoft Graph is an asset.

Experience integrating enterprise systems, knowledge repositories, databases, or business applications is an asset.

Understanding of responsible AI, privacy, security, data governance, records management, and intellectual property considerations.

Ability to troubleshoot complex issues, assess technical trade-offs, and explain recommendations clearly.

Strong analytical, problem-solving, documentation, communication, and collaboration skills.

You Have:

Demonstrates alignment with West Fraser values and acts with ownership, accountability, curiosity, and sound judgment.

Builds solutions around measurable business outcomes rather than technology alone.

Balances rapid experimentation with engineering quality, security, governance, and long-term supportability.

Validates AI-generated outputs and does not assume that model responses are accurate or complete.

Works effectively across business, data, integration, platform, security, privacy, and vendor teams.

Creates simple, reusable, and well-documented solutions instead of unnecessary complexity.

Can work independently while communicating decisions, risks, assumptions, dependencies, and next steps.

Continuously learns as AI platforms, models, risks, and engineering practices evolve.

Modern Capabilities to Emphasize Capability

What it means in this role

Build maintainable AI applications, assistants, agents, and workflow components.

Retrieval and grounding

Connect AI solutions t