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Senior Manager Delivery - Electrification Team (Construction)
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Toronto, ON
- Full-time
System applications for Electrification Infrastructure development.
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Husky Technologies
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Senior Software Engineer
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Senior Technical Specialist
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We are seeking an experienced Databricks Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based data platforms. The ideal candidate will have strong…
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AI Automation & Platform Analyst (Senior Analyst)
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Toronto, ON · Greater Toronto Area, ON
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We are hiring an AI Automation & Platform Engineer/Analyst to design and build the next generation of agent-driven workflows, automation systems, and internal tools.
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Senior Long-Term Planning Engineer
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Senior Full Stack Engineer
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The main responsibility of this role is to contribute to the development of RESTful APIs to support our front-end applications. You will modify existing APIs and create new APIs using both…
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Experienced Intermediate Software Engineer, Ruby on Rails
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Experis

AI DevOps Specialist (AI Enablement & Secure DevOps)

Canada

Contract

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Job details

Job type: Contract
Experience: ● 4+ years of hands-on experience in DevOps, Cloud Engineering, or System Administration, with at least 2 years focused specifically on AI/MLOps platform delivery.
Posting language: English
Posted: Oct 29, 2026

Original job description

AI DevOps Specialist (AI Enablement & Secure DevOps)

Contract Duration: 1 year + extension

Work Location: North York, ON.

Work Model: Hybrid preferred but open to considering remote

Operational Scope: Support across global Hardware Platform Solutions (HPS) Design Centers and secure testing labs (including the Hardware Platform Solutions (HPS) Innovation Lab and local RDL networks).

Project Objectives & Role Summary:

The AI DevOps Specialist is primarily responsible for the technical deployment, secure enablement, administration, and continuous optimization of the global HPS Artificial Intelligence (AI) sandbox, modeling, and software engineering toolchains.

This specialist will bridge the gap between AI development pipelines, secure networking infrastructure, and cloud platform services. They will take a hands-on lead in configuring foundational AI cloud services (primarily Google Cloud Platform - Vertex AI), managing user-facing web interfaces (LibreChat or Gemini Enterprise Agent Platform), implementing agentic workflow architectures (Flowise AI or Gemini Enterprise Agent Platform), ensuring secure, uninterrupted developer access to AI coding assistants (such as Claude Code, Gemini Code Assist, and Claude Sonnet/Opus models), and establishing robust cloud budget governance.

Core Responsibilities & Scope of Work

AI Sandbox Platform Administration & Deployment

Multi-Phase Sandbox Rollout: Own the deployment and lifecycle management of the HPS AI Sandbox environment across cloud and hybrid infrastructures.

Front-End User Interfaces: Deploy, configure, and maintain LibreChat (or similar web-based interfaces) to provide HPS engineers with safe, compliant, and localized access to LLMs.

Agentic Frameworks: Establish and configure Flowise AI or Gemini Enterprise Agent Platform for the design and orchestration of agentic AI workflows and LLM-backed applications.

Model Registry & Endpoints: Administer model deployment, model endpoint configurations, and vector databases within the sandbox environment.

GCP & Vertex AI Model Management

Project Governance: Oversee the HPS-designated GCP AI projects (e.g., gcp-ai-hps), including security permissions, service accounts, and IAM roles.

Model Enablement & Quota Tuning: Coordinate the provisioning and scale-out of advanced foundational models (including Anthropic Claude 3.5/3.6/3.7 suite [Opus, Sonnet, Haiku] and Google Gemini) in Vertex AI. Actively manage, troubleshoot, and resolve API quota restrictions with cloud providers.

Local Code Integration: Ensure developer command-line interfaces and local development terminals (such as Claude Code, VS Code, and GitKraken) seamlessly communicate with cloud model endpoints.

Secure DevOps & Network Integration (Zscaler & Firewall)

Lab Network Troubleshooting: Diagnose and resolve intermittent connection issues between HPS Design Labs (such as the Innovation Lab) and external AI resources or code repositories (e.g., troubleshooting DNS, routing timeouts, and SSL inspection blocks on GitHub).

Traffic Rules & Proxy Controls: Partner with Network Security to define, test, and troubleshoot Zscaler ZTNA app connectors, firewall rules, and proxy exceptions necessary to enable secure outbound AI API traffic while protecting proprietary codebase egress.

CI/CD Integration: Work alongside DevOps administrators to embed automated vulnerability checks, binary scanning, and AI-assisted testing steps in Azure DevOps, Jenkins, and GitHub pipelines.

Cloud Budget Governance & Financial Controls

Cost Allocation & Monitoring: Design and implement rigid budget monitoring, consumption alerts, and cost-attribution controls in GCP to track HPS developer usage.

Usage Auditing: Develop weekly/monthly utilization dashboards to track API token consumption, model call costs, and sandbox compute runtimes.

ROI Optimization: Provide recommendations on token limits, model pruning, caching strategies, and model choice (e.g., optimizing workloads to use Haiku or Flash models where appropriate to conserve budget).

AI Security, IP Protection & Compliance

Data Sovereignty Compliance: Enforce enterprise policies ensuring that no proprietary hardware schematics, PCB layouts, firmware source code, or IP are ingested into public training models.

Evaluation & Testing: Support the isolation of the HPS Innovation Lab to evaluate new open-source models, libraries, and AI security evaluation tools prior to general HPS rollout.

Technical Qualifications

Education & Experience

Bachelor’s degree in Computer Science, Software Engineering, DevOps, Cloud Engineering, or equivalent technical experience.

4+ years of hands-on experience in DevOps, Cloud Engineering, or System Administration, with at least 2 years focused specifically on AI/MLOps platform delivery.

Required Technical Skills

Cloud Platform Expertise (GCP): Advanced experience with Google Cloud Platform (GCP) and specifically Vertex AI / Model Garden, IAM, billing alerts, and VPC setups.

AI Toolchain & LLM Tooling: Proven experience deploying and maintaining containerized LibreChat architectures, Flowise AI (or LangChain/LlamaIndex equivalents), and local/cloud LLM APIs.

Networking & Security Engineering: Solid understanding of enterprise networking protocols (DNS, TCP/IP routing, NAT, SSL/TLS handshake) and secure access controls (Zscaler ZTNA, enterprise Firewalls).

Containerization & Orchestration: Strong proficiency with Docker, docker-compose, and Kubernetes to deploy scalable sandbox services.

Code Assist Integration: Familiarity with the configuration of developer-focused AI integrations like Claude Code, Gemini Code Assist, or MSFT Copilot CLI inside Linux/Mac environments.

Automation & Scripting: Strong scripting abilities in Python (specifically utilizing AI/ML libraries, request handling, and GCP SDKs) and Bash.

Preferred Certifications

Google Cloud Professional DevOps Engineer or Professional Cloud Architect

Google Cloud Professional Machine Learning Engineer

Certified Kubernetes Administrator (CKA)

HashiCorp Certified: Terraform Associate

Working Style & Competencies

Diagnostic Mindset: Exceptionally strong debugging skills for networking, package distribution, and cloud service interconnections.

Proactive Collaboration: Able to work cross-functionally with HPS Hardware Design Teams, Enterprise IT, Corporate Security, and external consulting suppliers (e.g., Elastify).

Detail-Oriented Documentation: High commitment to writing complete, clear Standard Operating Procedures (SOPs), system topologies, and budget management guidelines.

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More about this job

Responsibilities

Deploy, administer, and optimize the global AI sandbox and related toolchains, including Vertex AI services, model endpoints, user interfaces, and agentic workflow platforms. Integrate secure network access and CI/CD checks, protect proprietary intellectual property, and manage cloud budgets, usage monitoring, and cost optimization.

Requirements

Requires a bachelor’s degree in a relevant technical field or equivalent experience, plus at least four years in DevOps, cloud engineering, or system administration, including two years focused on AI/MLOps platform delivery. Candidates should have advanced GCP and Vertex AI experience, container and orchestration skills, enterprise networking and security knowledge, and Python and Bash scripting abilities; relevant cloud, Kubernetes, and Terraform certifications are preferred.

Skills

Google Cloud Platform
Vertex AI
AI/MLOps
LibreChat
Flowise AI
Cloud IAM
Networking and Troubleshooting
Zscaler ZTNA
Docker
Kubernetes
Python
Bash
CI/CD
Cloud Cost Governance
AI Security
Terraform

Visa sponsorship

Not detected in the job text

Keywords

Google Cloud Platform
Vertex AI
Model Garden
AI Sandbox
AI/MLOps
LibreChat
Flowise AI
LangChain
LlamaIndex
Anthropic Claude
Google Gemini
IAM
VPC
Zscaler ZTNA
Enterprise Firewalls
DNS
TCP/IP
SSL/TLS
Docker
Docker Compose
Kubernetes
Python
Bash
Azure DevOps
Jenkins
GitHub
CI/CD
Cloud Cost Governance
Google Cloud Professional DevOps Engineer
Google Cloud Professional Machine Learning Engineer

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