: This role is for an AI Developer who will help accelerate the team's adoption of Generative AI, Large Language Models (LLMs), and Agentic AI capabilities within the client platform. This individual will work hands-on with the client engineering and product teams to identify, design, build, and deploy practical AI use cases that improve the promotional planning experience and create measurable value for users.

A key part of the role will be to build capability within the existing team, helping developers develop the skills and engineering practices required to confidently build and maintain AI-enabled solutions themselves. The existing cloud-native application is built around independently deployed services, APIs, event-driven processing, and GCP technologies, providing a strong foundation for introducing AI capabilities into existing product workflows.

Technical Skills:

- Software Development: Solid proficiency in Python and experience developing production-grade back-end applications and APIs. Experience with SQL and modern software engineering practices is required.
- Generative AI & LLMs: Strong hands-on industry experience integrating LLMs into production applications, including prompt design, structured outputs, context management, model selection, and API-based model integration.
- Agentic AI : Experience designing AI agents and agentic workflows involving tool use, multi-step reasoning, orchestration, state management,



and interaction with enterprise systems and APIs.
- Retrieval & Grounding: Experience implementing grounding and retrieval patterns such as Retrieval-Augmented Generation (RAG), embeddings, vector search, and enterprise knowledge retrieval where appropriate
- AI Evaluation: Experience developing evaluation frameworks for AI applications, including measuring response quality, reliability, groundedness, task completion, latency, and other relevant product metrics.
- AI Engineering: Strong understanding of the challenges associated with production AI systems, including non-deterministic outputs, hallucinations, context management, failure handling, observability, model/version changes, and cost management.
- Cloud & Deployment: Experience deploying AI-enabled applications in cloud environments, preferably GCP, and working with containerized environments such as Docker and Kubernetes.
- API & Integration Design: S trong experience designing and consuming RESTful APIs and integrating AI capabilities into existing distributed applications and enterprise systems.
- Software Craftsmanship: Strong understanding of clean-code principles, automated testing, CI/CD, Git workflows, observability, and maintainable software architecture.
- Enterprise AI Platforms: Experience integrating applications with shared enterprise AI platforms, model gateways, or similar centralized AI capabilities is highly desirable.

📌 Artificial Intelligence Engineer (Greater Toronto Area)
🏢 Digitalogia
📍 Greater Toronto Area