AI Developer

We are looking for an AI Engineer with a strong cloud, software-development and full-stack engineering background to design and build secure, scalable and user-centred AI applications. This role focuses on turning AI models, agents and data capabilities into complete business solutions through front-end development, back-end services, APIs, enterprise integrations and production-quality engineering. 

Canada Life is one of Canada’s leading insurance carriers and part of a strong and mature global financial company. Canada Life has embarked on a transformation that is changing the way we work and how we help our customers. You will be part of a core AI delivery team with a strong commitment to transforming our business through technology, data and analytics. You will work closely with Data Scientists, AI Platform Engineers, Data Engineers, MLOps Engineers, Business Analysts, Solution Architects, Product Owners, governance partners and business subject-matter experts. 

What you’ll do: 

• Partner with business, data-science and technology stakeholders to translate use cases, user journeys and non-functional requirements into complete AI application designs. 

• Design and build full-stack AI applications, including accessible and responsive front ends, back-end services, APIs, data stores and enterprise integrations. 

• Develop intuitive user experiences that enable people to interact effectively with AI capabilities, review source evidence, provide feedback and complete human-in-the-loop activities. 

• Build production-grade back-end services in Python and other appropriate technologies, applying modular design, separation of concerns, secure coding and reusable engineering patterns. 

• Design and implement REST APIs, service contracts, validation, error handling, authentication and integration patterns connecting AI solutions with enterprise systems and data sources. 

• Engineer generative-AI and agentic solutions that combine models, retrieval, tools, APIs, business rules and workflow components while selecting the simplest effective architecture. 

• Implement retrieval-augmented generation and intelligent-document-processing components, including ingestion, parsing, chunking, indexing, retrieval, grounding and citation experiences. 

• Integrate with approved cloud and AI platform capabilities for model access, orchestration, identity, security, observability and deployment without duplicating shared platform ownership. 

• Develop automated unit, integration, end-to-end and regression tests covering application behaviour, AI interactions, API contracts, security controls and failure conditions. 

• Containerise applications and support their deployment across sandbox, development, QA, UAT and production environments in collaboration with MLOps and platform engineering teams. 

• Design for performance, scalability, availability, recoverability, cost efficiency and supportability, including effective logging, tracing, telemetry and operational diagnostics. 

• Apply responsible-AI, privacy, security, model-risk and data-governance requirements throughout solution design and implementation, including guardrails, traceability and human oversight. 

• Produce production-ready code, technical documentation, architecture and decision records, API specifications, test evidence and operational runbooks. 

• Evaluate emerging AI models, frameworks, developer tools and vendor solutions through structured prototypes and recommend approaches based on quality, maintainability, security, cost and enterprise fit. 

• Contribute reusable application components, SDKs, integration patterns, front-end elements and engineering guidance, while providing technical leadership through design reviews, code reviews and peer coaching. 

What you bring: 

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering or a related discipline, or equivalent practical experience. 

• 3+ years of hands-on experience building and delivering production software applications, preferably including cloud-hosted AI or data-intensive solutions. 

• Strong software-development skills in Python and at least one modern front-end framework such as React with the ability to build both user interfaces and back-end services. 

• Demonstrated experience with API engineering, enterprise integration, database or storage integration, object-oriented and modular design, source control and automated testing. 

• Practical experience with cloud-native engineering on Microsoft Azure and Databricks, including containers, identity and access, secrets, configuration, networking, observability and environment management. 

• Hands-on experience integrating generative AI, large language models, retrieval or agentic capabilities into applications and business workflows. 

• Ability to build secure, scalable and supportable solutions within enterprise architecture, responsible-AI, privacy, security and data-governance requirements. 

• Strong problem-solving, communication and collaboration skills, with the ability to work across business, data science, engineering, architecture, governance and external delivery partners. 

Assets: 

• Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, Document Intelligence, content understanding or comparable cloud AI services. 

• Experience with Databricks, MLflow, Spark, Unity Catalog or Databricks generative-AI and retrieval capabilities. 

• Experience with agent frameworks and tool-integration approaches such as Microsoft Agent Framework, Semantic Kernel, LangChain, LangGraph or MCP. 

• Knowledge of CI/CD, GitLab, Azure DevOps, Kubernetes, infrastructure as code and automated security or quality controls. 

• Experience in financial services, insurance or another regulated industry, including claims, underwriting, fraud, knowledge management or document-modernisation solutions. 

Given the size and scope of our organisation, we have the flexibility for this position to be located in the following head office locations: Toronto, London, Winnipeg.