Job Summary
We are seeking a highly skilled incumbent with deep, hands-on experience designing, implementing, and optimizing enterprise-scale solutions on Google Cloud Platform (GCP), with proven delivery experience on Google Gemini Enterprise. This is a highly technical role requiring an individual who not only architects solutions but is also capable of personally building, deploying, troubleshooting, and optimizing cloud-native platforms, AI agents, and applications. The incumbent serves as the organization's trusted Google Cloud and Gemini Enterprise expert, driving cloud and AI strategy and delivering scalable, secure, governed, and cost-effective solutions that accelerate business outcomes.
Main Responsibilities
Architecture and Design
- Lead the hands-on design and implementation of end-to-end cloud solutions on Google Cloud Platform (GCP).
- Architect secure, scalable, resilient, and highly available enterprise cloud environments.
- Develop reference architectures, technical standards, cloud adoption roadmaps, and solution blueprints.
- Design hybrid and cloud-native architectures supporting business-critical applications and services.
- Define integration patterns across applications, platforms, data ecosystems, and third-party solutions.
- Translate business requirements into scalable and sustainable technical solutions.
Engineering, Platform and AI Delivery
- Personally build, configure, test, deploy, and support production-grade solutions across the GCP ecosystem.
- Develop and maintain Infrastructure as Code (IaC) using Terraform and cloud automation best practices.
- Build and deploy containerized solutions using Google Kubernetes Engine (GKE), Cloud Run, and other cloud-native services.
- Design and implement CI/CD pipelines supporting automated deployment and operational excellence.
- Lead cloud migration, modernization, and transformation initiatives from on-premises and multi-cloud environments.
- Troubleshoot and resolve complex technical issues across cloud infrastructure, applications, integrations, and platforms.
Gemini Enterprise, Agentic AI and Cloud Expertise
- Act as the hands-on technical lead and architect for the enterprise Gemini Enterprise platform, including reference architecture, landing zone, configuration, operating standards, and governance.
- Configure and administer the Gemini Enterprise app, including identity federation, access controls, agent gallery, licensing, and usage analytics.
- Design and implement data connectors, data stores, and secure access patterns across enterprise systems such as SharePoint, OneDrive, Google Drive, ServiceNow, Jira, Salesforce, BigQuery, Databricks, and Data Lakehouse platforms.
- Build, deploy, and operate custom AI agents using Agent Development Kit (ADK), Agent Studio, Agent Runtime, Python, APIs, and Model Context Protocol (MCP).
- Define the enterprise AI reference architecture, including patterns for retrieval-augmented generation (RAG), single and multi-agent systems, tool use, human-in-the-loop, model evaluation, and model fine-tuning.
- Implement agent governance and security using Agent Gateway, Agent Identity, Agent Registry, IAM policies, Model Armor, VPC Service Controls, Sensitive Data Protection, and responsible AI guardrails.
- Prepare and present AI and cloud solution architectures to enterprise architecture, cybersecurity, and governance review boards.
Working Conditions
The role has standard working conditions in an office environment with a regular workweek from Monday to Friday and is eligible to participate in CN's hybrid work model. The role requires occasional business travel within Canada and the United States in accordance with CN policy.
Experience
Cloud, AI and Solution Architecture
- 10+ years of overall technology experience, including enterprise architecture, solution architecture, system architecture, cloud engineering, or related technology roles
- Minimum 5 years of experience in Solution Architecture and/or Systems Architecture roles
- Minimum 7 years of hands-on experience designing and implementing cloud solutions
- Expert-level, end-to-end hands-on experience with Google Cloud Platform in large enterprise environments
- Demonstrated hands-on experience architecting and delivering at least one enterprise Gemini Enterprise implementation in production, including connectors, identity integration, access controls, and governance
- Minimum 3 years of experience in an AI, machine learning, or Generative AI architecture role, with solutions delivered to production
- Experience with Databricks on Google Cloud Platform, transportation, logistics, supply chain, or industrial organizations*
- Candidates with an equivalent combination of education, training, and experience will be considered
* Any requirement marked with (*) will be considered an asset (not mandatory)
Education/Certification/Designation
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field
- Google Professional Cloud Architect Certification, Google Professional Cloud Security Engineer Certification, Google Professional Machine Learning Engineer Certification, Google Cloud Generative AI Leader Certification, Google Cloud Professional Agentic Architect Certification (Beta), TOGAF, ITIL, or SAFe Architect certification*
- Candidates with equivalent practical experience, education, or training will be considered
* Any requirement marked with (*) will be considered an asset (not mandatory)
Competencies
- We Care About Each Other
- We Strive for Excellence
- We Succeed Together
- We Lead Responsibly
- Compliance
- Business acumen
- High-Impact Execution
- Conveying the vision
- Coordination
Technical Skills/Knowledge
- Expert knowledge of Google Cloud Platform services including Compute Engine, GKE, Cloud Run, Cloud Functions, Cloud Storage, BigQuery, Pub/Sub, Cloud SQL, AlloyDB, IAM, and Google Cloud Operations Suite
- Hands-on experience with Gemini Enterprise, Gemini Enterprise Agent Platform, Agent Development Kit (ADK), Agent Studio, Agent Runtime, APIs, MCP, RAG, agent evaluation, and production AI agent operations
- Deep understanding of LLM architecture patterns, including retrieval-augmented generation, agents and multi-agent orchestration, prompt engineering, embeddings, fine-tuning, vector databases, and responsible AI controls
- Experience with cloud networking, identity and access management, encryption, key management, secrets management, security controls, compliance, and governance frameworks
- Strong experience with Terraform, Infrastructure as Code, DevOps, CI/CD, Site Reliability Engineering, Kubernetes, containerization, and cloud-native application architectures
- Experience designing modern data, analytics, streaming, batch, real-time, MLOps, LLMOps, AgentOps, and AI governance solutions using GCP-native technologies, Databricks, Kafka, and Data Lakehouse platforms
- Knowledge of AI-specific risks including prompt injection, data leakage, hallucination, excessive agent autonomy, model drift, quality measurement, guardrails, and responsible AI practices
- Excellent communication, stakeholder management, architecture review, technical leadership, mentoring, and strategic cloud planning skills