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

Solid 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.

📌 DevOps Platform Engineer | AI & LLMOps Infrastructure (Toronto)
🏢 Ku0026K Global Talent Solutions
📍 Toronto