Expert, AI EngineerJob SummaryThe incumbent is responsible fordesigning, building, integrating, and operationalizing enterprise artificialintelligence (AI) solutions that support CN's next generation of data,analytics, automation, and agentic capabilities. The role translates businessopportunities into production-grade AI applications, AI agents, intelligentworkflows, reusable components, and platform-integrated services. The incumbentworks across software engineering, data engineering, machine learning, andgenerative AI to deliver secure, governed, reliable, and scalable AI solutionsusing CN-approved platforms and patterns. The role collaborates with AIarchitects, platform engineers, AI operations, data scientists, data engineers,business partners, cybersecurity, governance, architecture, and vendor teams tomove AI use cases from concept to production.
Main ResponsibilitiesAI Engineering andSolution Delivery- 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
Data and PlatformIntegration
- 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
Governance, Operations and ContinuousImprovement- 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
RequirementsExperience- AI Engineering
- 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*
Any requirementabove marked with (*) would be considered as an asset (not mandatory)
Competencies- Applies critical thinking
- Knows the business and stays current on industry needs
- Collaborates with others and shares information
- Communicates with impact
- Identifies needs and finds solutions to create value for all stakeholders
- Leads by example for the safety and security of all
- Identifies potential safety and security risks
TechnicalSkills/Knowledge - 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
Education/Certification/Designation- Bachelor's Degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Information Technology, Engineering, or a related field, or equivalent experience
- Google Cloud, Vertex AI, Machine Learning Engineer, Data Engineer, or related cloud or AI certification*
- Any requirement above marked with (*) would be considered as an asset (not mandatory)
