Manager AI Engineering-CA

Role overview

AI delivery and integration

• Develop and deploy AI applications, agents, and workflows; build generative AI, machine learning, and automation solutions.
• Implement RAG, prompt engineering, model evaluation, workflow automation, reusable APIs, services, and components.
• Integrate enterprise applications and data platforms; develop secure and scalable data pipelines and interfaces.
• Contribute to Databricks, Gemini Enterprise, Vertex AI, and related platforms; align solutions with architecture and data quality needs.

Governance and operations

• Apply governance, security, privacy, Responsible AI, and model risk controls throughout solution delivery.
• Support deployment, monitoring, troubleshooting, quality evaluation, red-teaming, hallucination mitigation, and continuous improvement.
• Document designs, decisions, limitations, and operational requirements for auditability and handover.

Experience and education

• Requires 2–5 years in software engineering, data engineering, machine learning engineering, AI engineering, or related technology delivery.
• Experience with enterprise AI applications and generative AI, LLMs, RAG, vector search, embeddings, prompt engineering, agents, and model evaluation.
• Asterisk-marked platform and operational experience is an asset, not mandatory.
• Requires a relevant bachelor’s degree or equivalent experience; related cloud or AI certifications are assets, not mandatory.
• Requires Python, SQL, APIs, Git, CI/CD, testing, and software engineering skills; knowledge of AI frameworks and data governance is expected.

Working conditions

• Standard office working conditions, Monday to Friday; eligible to participate in CN’s hybrid work model.
• Work involves managing priorities and delivering within established timelines, with team support.