Expert, AI Engineer

Canadian National Railway CompanyBrampton, ON

About The Position

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.

Requirements

  • Between 2 to 5 years of experience in software engineering, data engineering, machine learning engineering, AI engineering, or related technology delivery roles
  • Experience building enterprise copilots, chatbots, document intelligence solutions, workflow assistants, AI agents, or agentic applications
  • Experience with generative AI, large language models (LLMs), Retrieval Augmented Generation (RAG), vector search, embeddings, prompt engineering, agents, and model evaluation
  • Experience 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
  • Strong programming skills in Python and Structured Query Language (SQL), with experience using APIs, Git, CI/CD, testing, and software engineering practices
  • Knowledge of AI frameworks and tools such as LangChain, LangGraph, CrewAI, AutoGen, FastAPI, Streamlit, Docker, Kubernetes, or related technologies
  • Understanding of data governance, data quality, metadata, lineage, security, privacy, Responsible AI, and enterprise technology delivery practices
  • Bachelor’s Degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Information Technology, Engineering, or a related field, or equivalent experience

Nice To Haves

  • Google Cloud, Vertex AI, Machine Learning Engineer, Data Engineer, or related cloud or AI certification

Responsibilities

  • Design, develop, test, and deploy AI-powered applications, AI agents, and intelligent workflows that address business needs and deliver measurable value
  • Build and enhance generative AI, machine learning, and automation solutions using CN-approved engineering patterns, platforms, and controls
  • Implement Retrieval Augmented Generation (RAG), prompt engineering, model evaluation, and AI workflow automation patterns to support scalable production solutions
  • Develop reusable application programming interfaces (APIs), services, and components that accelerate AI delivery and promote consistency across use cases
  • Integrate AI solutions with enterprise applications, data platforms, business processes, and operational workflows
  • Develop and optimize data pipelines, services, and interfaces that support reliable, secure, and scalable AI applications
  • Contribute to enterprise AI platforms, including Databricks, Gemini Enterprise, Vertex AI, and related technologies, to support solution delivery
  • Collaborate with data engineers, platform teams, and business partners to ensure AI solutions are aligned with enterprise architecture, data quality, and operational requirements
  • Apply AI governance, security, privacy, Responsible AI, and model risk controls throughout the solution lifecycle
  • Support production deployment, monitoring, troubleshooting, quality evaluation, red-teaming, hallucination mitigation, and continuous improvement of AI solutions
  • Document technical designs, decisions, limitations, and operational requirements to support maintainability, auditability, and successful handover
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