Forward Deployed AI Engineer

Team: AI COE

Location: Toronto

Commitment: Full Time

Workplace Type: hybrid

What You Will Be Responsible For:

You will play a lead technical role in designing and delivering AI-enabled solutions across the enterprise.

1. Build & Ship AI Applications (Primary Focus)

• Design, develop, and deploy AI-powered applications and workflows
• Write production-quality code across:
• Backend services and APIs
• AI orchestration layers and agents
• Enterprise integrations
• Rapidly prototype solutions and iterate them into scalable production systems
• Own delivery end-to-end: build, test, deploy, monitor, and improve

2. Design Practical, Scalable AI Systems

• Translate use cases into clear, implementable system designs
• Make architecture decisions that balance:
• Speed of delivery
• Scalability and reliability
• Cost and operational efficiency
• Define patterns for:
• API-first integrations
• AI orchestration and workflows
• Reusable services and components
• Ensure systems are simple enough to build quickly, but structured enough to scale

3. Integrate AI into Real Enterprise Workflows

• Embed LLM capabilities into products, internal tools, and business processes
• Build and maintain APIs and system integrations
• Implement agent workflows and orchestration logic that solve real operational problems
• Optimize systems for performance, resilience, and cost efficiency

4. Partner with Business & Deliver Outcomes

• Work directly with stakeholders to understand problems and validate solutions
• Translate requirements into working software quickly (days/weeks, not months)
• Iterate based on feedback and usage to drive measurable impact

5. Contribute to Engineering Standards & Reuse

• Build and contribute to shared libraries, templates, and services
• Establish practical patterns based on real implementations
• Help evolve internal platforms through code and working solutions, not just design artifacts

6. Build Within a Governed AI Environment

• Implement secure and reliable AI solutions in practice, including:
• Prompt safety and validation
• Injection/misuse prevention
• Observability and traceability
• Align implementations with enterprise security, privacy, and compliance requirements

Technology Environment

• Cloud & Platform: Microsoft ecosystem (Azure)
• AI Models: Claude and other enterprise-approved LLMs
• Architecture Style: API-first, event-driven, and modular services
• Core Focus:
• AI application engineering
• Orchestration and agent workflows
• Enterprise integrations

What you bring:

Hands-On Engineering Strength (Critical)

• Proven ability to build and ship production systems at scale
• Strong experience in:
• Backend development and API design
• Cloud-native systems (Azure preferred)
• Integration-heavy, distributed applications
• Comfortable operating in a high-output, hands-on environment

System Design & Architecture Judgment

• Ability to design clean, practical architectures that support real-world constraints
• Experience making trade-offs across:
• delivery speed vs scalability
• simplicity vs flexibility
• Can move fluidly between coding and design thinking

AI / GenAI Development

• Hands-on experience building LLM-powered applications in production
• Strong understanding of:
• Prompt design and evaluation
• Agent-based workflows and orchestration
• Integrating AI into production systems
• Ability to debug, tune, and improve AI behavior in code

Execution Mindset

• Bias toward shipping and learning from production usage
• Comfortable moving from idea → prototype → production
• Strong ownership: you build it, you run it