AI Engineer

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 Australia.

• 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.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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