Select how often (in days) to receive an alert: AI DevOps Specialist (AI Enablement & Secure DevOps)

Date:

Sep 21, 2026 Location:

Toronto, ON, CA Project Objectives & Role Summary

The

AI DevOps Specialist

is primarily responsible for the technical deployment, secureenablement, administration, and continuous optimization of Celestica’s global HPS ArtificialIntelligence (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

Multi-Phase Sandbox Rollout:

Own the deployment and lifecycle management of the HPS AI Sandbox setting 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 localizedaccess to LLMs. Agentic Frameworks:

Establish and configure Flowise AI or Gemini Enterprise AgentPlatform for the design and orchestration of agentic AI workflows and LLM-backedapplications. Model Registry & Endpoints:

Administer model deployment, model endpointconfigurations, and vector databases within the sandbox environment. 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 ofadvanced foundational models (including Anthropic Claude 3.5/3.6/3.7 suite [Opus,Sonnet, Haiku] and Google Gemini) in Vertex AI. Actively manage, troubleshoot, andresolve API quota restrictions with cloud providers. Local Code Integration:

Ensure developer command-line interfaces and localdevelopment terminals (such as Claude Code, VS Code, and GitKraken) seamlesslycommunicate with cloud model endpoints. Lab Network Troubleshooting:

Diagnose and resolve intermittent connection issuesbetween HPS Design Labs (such as the Innovation Lab) and external AI resources or coderepositories (e.g., troubleshooting DNS, routing timeouts, and SSL inspection blocks onGitHub). Traffic Rules & Proxy Controls:

Partner with Network Security to define, test, andtroubleshoot Zscaler ZTNA app connectors, firewall rules, and proxy exceptions necessaryto enable secure outbound AI API traffic while protecting proprietary codebase egress. CI/CD Integration:

Work alongside DevOps administrators to embed automatedvulnerability checks, binary scanning, and AI-assisted testing steps in Azure DevOps,Jenkins, and GitHub pipelines. 4. 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 tokenconsumption, 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). 5. AI Security, IP Protection & Compliance Data Sovereignty Compliance:

Enforce enterprise policies ensuring that no proprietaryhardware schematics, PCB layouts, firmware source code, or IP are ingested into publictraining models. Evaluation & Testing:

Support the isolation of the HPS Innovation Lab to evaluate newopen-source models, libraries, and AI security evaluation tools prior to general HPS rollout. Education & Experience

Bachelor’s degree in Computer Science, Software Engineering, DevOps, CloudEngineering, 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 containerizedLibreChat architectures, Flowise AI (or LangChain/LlamaIndex equivalents), andlocal/cloud LLM APIs. Networking & Security Engineering: Solid understanding of enterprise networkingprot

📌 AI DevOps Specialist (Toronto)
🏢 Celestica
📍 Toronto