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Highest Level Division: Technology Operations & EnablementPosting Start Date: 11/2/26Posting End Date: 09/26/26 11:59 EDTHiring Salary Range:
$110000
- 144000This range represents the hiring range. Salaries are determined individually depending on skills and experience. You will also be eligible to an annual bonus, employee share investment program (ESIP), Pension, Benefits and other perks.
At CN, we are committed to our pursuit of excellence. We take pride in delivering work of the highest standard, continually innovating to shape the railroad of the future. As responsible railroaders, we make the right decisions every time, putting safety first, acting with unwavering integrity, and holding ourselves accountable for our actions. We believe in succeeding together—by fostering inclusivity and collaboration, we sustainably deliver exceptional results for our customers.
Job Summary
The incumbent is responsible for designing, building, integrating, and operationalizing enterprise artificial intelligence (AI) solutions that support CN's next generation of data, analytics, automation, and agentic capabilities. The role translates business opportunities into production-grade AI applications, AI agents, intelligent workflows, reusable components, and platform-integrated services. The incumbent works across software engineering, data engineering, machine learning, and generative AI to deliver secure, governed, reliable, and scalable AI solutions using CN-approved platforms and patterns. The role collaborates with AI architects, platform engineers, AI operations, data scientists, data engineers, business partners, cybersecurity, governance, architecture,



and vendor teams to move AI use cases from concept to production.
Main Responsibilities
AI Engineering and Solution DeliveryDesign, develop, test, and deploy AI-powered applications, AI agents, and intelligent workflows that address business needs and deliver measurable valueBuild and enhance generative AI, machine learning, and automation solutions using CN-approved engineering patterns, platforms, and controlsImplement Retrieval Augmented Generation (RAG), prompt engineering, model evaluation, and AI workflow automation patterns to support scalable production solutionsDevelop reusable application programming interfaces (APIs), services, and components that accelerate AI delivery and promote consistency across use casesData and Platform Integration
Integrate AI solutions with enterprise applications, data platforms, business processes, and operational workflowsDevelop and optimize data pipelines, services, and interfaces that support reliable, secure, and scalable AI applicationsContribute to enterprise AI platforms, including Databricks, Gemini Enterprise, Vertex AI, and related technologies, to support solution deliveryCollaborate with data engineers, platform teams, and business partners to ensure AI solutions are aligned with enterprise architecture, data quality, and operational requirementsGovernance, Operations and Continuous Improvement
Apply AI governance, security, privacy,



Responsible AI, and model risk controls throughout the solution lifecycleSupport production deployment, monitoring, troubleshooting, quality evaluation, red-teaming, hallucination mitigation, and continuous improvement of AI solutionsDocument technical designs, decisions, limitations, and operational requirements to support maintainability, auditability, and successful handoverWorking Conditions
The role has standard working conditions in an office workplace with a regular workweek from Monday to Friday and is eligible to participate in CN's hybrid work model. The role involves managing priorities and delivering work within established timelines, with support from the team to ensure sustainable and effective workload management. RequirementsExperience
Between 2 to 5 years of experience in software engineering, data engineering, machine learning engineering, AI engineering, or related technology delivery rolesExperience building enterprise copilots, chatbots, document intelligence solutions, workflow assistants, AI agents, or agentic applicationsExperience with generative AI, large language models (LLMs), Retrieval Augmented Generation (RAG), vector search, embeddings, prompt engineering, agents, and model evaluationExperience with Google Cloud Platform, Vertex AI, Gemini Enterprise, Databricks, Spark/PySpark, Delta Lake, Unity Catalog, MLflow, or comparable cloud AI and data platforms*Experience implementing observability;
quality evaluation;
red-teaming;
hallucination mitigation;
safety controls;
and continuous improvement practices for AI systems*Candidates with an equivalent combination of education, training, and experience will be consideredAI Engineering
Between 2 to 5

📌 Expert Ai Engineering-Ca (Canada)
🏢 Canadian National
📍 Canada