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Added 10 mins ago
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Cloud Solution Architect (CSA) - Cloud & AI Data
Microsoft
Ottawa, ON
- Full-time
We’re looking for a Cloud Solution Architect (CSA) focused on Cloud & AI Data to help customers build unified, governed, secure, and AI-ready data estates on Microsoft Azure. In this…
Added 24 mins ago
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AI Solution Lead
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Toronto, ON
- Full-time
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Added 24 mins ago
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Added 24 mins ago
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Systems Architect
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- Full-time
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Added 24 mins ago
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Added 24 mins ago
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Added 54 mins ago
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…comprehensive gap analyses. Remediation Planning: Ability to translate technical findings into actionable remediation recommendations and risk mitigation strategies aligned with industry…
Added 54 mins ago
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Principal Architect
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Added 3 hours ago
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DevOps Platform Engineer | AI & LLMOps Infrastructure
K&K Global Talent Solutions INC.
ON · Ontario, Canada
- Full-time
Infrastructure as Code & Automation: Architect, deploy, and manage highly reliable multi-account AWS environments using Terraform, CloudFormation, or AWS CDK.
Added 3 hours ago
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Technologies numériques - Opérations manufacturières : Analyste des systèmes MES / Digital Technologies - Manufacturing Operations: MES Systems Analyst
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Added 3 hours ago
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Senior Software Developer
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Added 3 hours ago
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Added 3 hours ago
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Principal Data Solutions Architect- AI/ML
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Added 4 hours ago
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Analyste-programmeur(se) full stack
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- Full-time
Wepoint is the architect of major transformations for businesses and public sector organizations.

K&K Global Talent Solutions INC.

DevOps Platform Engineer | AI & LLMOps Infrastructure

ON, Canada
Ontario, Canada, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Nov 8, 2026
Experience: Experience: 3–5+ years of hands-on platform engineering, cloud architecture, and CI/CD operations within complex enterprise environments.
Posting language: English
Posted: Oct 9, 2026

Original job description

Position: DevOps Platform Engineer | AI & LLMOps Infrastructure

Location: Onsite- Toronto, ON

Job type: Full-Time/Permanent Hiring

As an Agent & DevOps Platform Engineer, you will design, scale, and secure the foundational infrastructure that powers our next-generation developer platforms and autonomous AI/LLM agent frameworks. This is a highly specialized role bridging the gap between advanced cloud-native Platform Engineering and cutting-edge LLMOps. You will not just build standalone AI applications - you will build the highly automated, multi-account AWS environments, robust CI/CD pipelines, and secure IAM patterns that allow autonomous agents and developers to ship code reliably, securely, and at enterprise scale.

Key Responsibilities

Infrastructure as Code & Automation: Architect, deploy, and manage highly reliable multi-account AWS environments using Terraform, CloudFormation, or AWS CDK.
Agent & Tooling Orchestration: Build, deploy, and scale enterprise developer platforms, integrating agentic orchestration frameworks (e.g., LangChain, LlamaIndex) and custom toolchains into production pipelines.
Bulletproof Identity & Access: Implement advanced AWS IAM architectures under the principle of least privilege, designing secure cross-account access, service-to-service authentication, and Kubernetes service accounts.
Container Platform Engineering: Package and orchestrate distributed container systems using Docker and enterprise container platforms (Amazon EKS, ECS, or Kubernetes), configuring robust networking, ingress, and workload security.
LLMOps & Platform Observability: Build the guardrails for production AI workloads, implementing systems for prompt management, tool execution, model evaluation, observability (CloudWatch), and cost-reliability metrics.
Scale CI/CD & DevSecOps: Treat security as a core software design constraint. Author high-quality reusable pipeline patterns, build automation workflows, and enable decentralized DevOps practices across multiple business units.
System Ownership & Culture: Act as a technical mentor whose coaching is reflected in clean working code, robust pipelines, and reusable modules. Define and solve core platform bottlenecks in ambiguous environments.

Required Qualifications

Experience: 3–5+ years of hands-on platform engineering, cloud architecture, and CI/CD operations within complex enterprise environments.
Programming: Strong proficiency in Python for infrastructure tooling, API integrations, agent services, and advanced production scripting.
Technical Ecosystems: Working competence in at least one additional development ecosystem (e.g., Java, .NET, Node.js, or Groovy).
Cloud Native & Containers: Deep production experience with AWS cloud-native deployments and container orchestration via Docker and Kubernetes / Amazon EKS / ECS.
AWS Security Mastery: Comprehensive knowledge of complex AWS IAM structures, including roles, policies, trust relationships, and secure machine-to-machine authentication.
AI/LLMOps Engineering: Direct experience building, deploying, or scaling AI/LLM-based tools or agents in or near production. Solid understanding of prompt management, tool usage, and agent testing.
Methodology: Strong grasp of Git-based workflows, release management at scale, and an uncompromising DevSecOps approach to system design.

Nice-to-Have Skills

Hands-on usage of advanced AWS ecosystem tools: ECR, Lambda, Bedrock, CloudWatch, S3, Secrets Manager, Systems Manager, and VPC networking.
Familiarity with Kubernetes internals: Helm, service accounts, ingress controllers, and network policies.
Prior experience working with framework tools like LangChain or LlamaIndex.
Experience driving agile delivery models using Scrum, Kanban, or SAFe across multiple teams.

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More about this job

Responsibilities

Design and operate secure, scalable multi-account AWS infrastructure, container platforms, and reusable CI/CD pipelines for enterprise developer platforms and AI/LLM agents. Implement agent orchestration, production LLMOps guardrails and observability, while mentoring teams and resolving platform bottlenecks.

Requirements

Requires 3–5+ years of platform engineering, cloud architecture, and CI/CD experience, strong Python skills, and production expertise with AWS, containers, Kubernetes, and complex IAM. Candidates must also have direct experience building or scaling AI/LLM tools or agents, plus knowledge of Git-based release workflows and DevSecOps.

Skills

AWS
Terraform
CloudFormation
AWS CDK
Python
CI/CD
DevSecOps
AWS IAM
Docker
Kubernetes
Amazon EKS
Amazon ECS
LLMOps
LangChain
LlamaIndex
CloudWatch

Visa sponsorship

Not detected in the job text

Keywords

Platform Engineering
AWS
Terraform
CloudFormation
AWS CDK
Python
Java
.NET
Node.js
Groovy
CI/CD
DevSecOps
AWS IAM
Cross-Account Access
Docker
Kubernetes
Amazon EKS
Amazon ECS
LLMOps
AI Agents
LangChain
LlamaIndex
Prompt Management
CloudWatch
Amazon ECR
AWS Lambda
Amazon Bedrock
Amazon S3
AWS Secrets Manager
Helm

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