AI Engineer

Team: IT

Location: Canada

Commitment: Full-time

Workplace Type: remote

Accountabilities:

• Build full-stack features and components for AI pilots and prototypes, including backend services, REST APIs, data connectors, and frontend implementations.

• Develop clean, well-documented code designed for reliable handoff to teams responsible for maintaining operational systems.

• Integrate AI applications with cloud platforms, data infrastructure, internal APIs, API gateways, and data sources such as Snowflake.

• Follow established API gateway, data access, schema, and integration standards while collaborating with internal platform teams.

• Contribute to an internal AI agent library by implementing reusable agent patterns, writing tests, instrumenting traces, and documenting module behavior.

• Build and maintain AI agent capabilities, including tool integrations, API connectors, evaluation harnesses, and observability instrumentation.

• Develop baseline evaluations that help verify AI agent behavior, reliability, and consistency before systems are relied upon.

• Work with frontier model APIs, agentic frameworks such as LangGraph and CrewAI, and MCP server integrations.

• Collaborate closely with AI and product experience engineers to implement frontend components, connect user interfaces with backend services, and contribute to shared component libraries.

• Support technical scoping for new initiatives by identifying integration dependencies, investigating technical unknowns, and estimating implementation effort.

• Participate in emerging technology evaluations by building proof-of-concept implementations and contributing findings to technology assessments.

• Apply observability and testing practices throughout development so AI systems are traceable, inspectable, and maintainable.

• Contribute to an AI engineering environment where successful prototypes can evolve into reusable, production-oriented capabilities.

Requirements:

• Strong full-stack software engineering fundamentals, including backend development, REST APIs, cloud-native service patterns, data integrations, and frontend implementation.

• Demonstrated experience building something real with an LLM or AI system, such as an AI agent, RAG pipeline, tool-calling integration, or comparable application.

• Genuine curiosity about AI systems and a strong interest in following developments in frontier models, agentic architectures, and AI-assisted software development.

• Practical understanding of RAG, tool calling, LLM APIs, and modern AI application patterns.

• Strong ability to write clean, maintainable, well-documented code and learn quickly in a technically demanding environment.

• Ability to absorb technical direction, ask thoughtful questions, work through ambiguity, and contribute without requiring fully defined specifications.

• Experience with Python and/or TypeScript is highly valuable.

• Familiarity with LangChain, LangGraph, CrewAI, or comparable agent frameworks is an asset.

• Experience with Snowflake, BigQuery, or another cloud-based data platform is beneficial.

• Understanding of RAG architectures, vector databases such as pgvector, Pinecone, or Weaviate, and basic AI evaluation harnesses is preferred.

• Familiarity with AI observability concepts, including traces and logs, and tools such as Langfuse or LangSmith, is an advantage.

• Strong collaboration and communication skills, with the ability to work effectively alongside principal-level engineers and cross-functional technical partners.

• A proactive, fast-learning, delivery-oriented mindset and willingness to experiment with emerging AI technologies.

• Candidates must be legally authorized to work in Canada, as employment sponsorship is not provided for this position.

Benefits:

• Fully remote opportunity within Canada.

• Starting annual salary range of $95,000–$115,000 CAD, with compensation determined by relevant skills, education, qualifications, experience, performance, business needs, and geographic location.

• Virtual-first working environment with flexible work arrangements.

• Opportunity to work hands-on with modern AI engineering technologies, including frontier model APIs and agentic frameworks.

• Exposure to real-world AI application development, evaluation, observability, and cloud data infrastructure.

• Opportunity to contribute reusable AI engineering patterns and tools used across a broader technical organization.

• Collaborative environment alongside experienced AI and software engineers.

• Learning and development opportunities focused on emerging technologies and practical AI engineering.

• High-impact work involving innovative AI pilots and prototypes that can progress toward operational systems.

• Culture centered on innovation, collaboration, data-driven decision-making, execution, trust, and continuous learning.