DevOps Platform Engineer | AI & LLMOps Infrastructure

Location:

Onsite
- Toronto, ON

Job type:
Full-Time/Permanent Hiring

As an  Agent & Dev Ops 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, Cloud Formation, or AWS CDK .

Agent & Tooling Orchestration:  Build, deploy, and scale enterprise developer platforms, integrating  agentic orchestration frameworks (e.g., Lang Chain, Llama Index)  and custom tool chains 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 (Cloud Watch), and cost-reliability metrics .

Scale CI/CD & Dev Sec Ops :  Treat security as a core software design constraint. Author high-quality reusable pipeline patterns, build automation workflows, and enable decentralized Dev Ops 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 Dev Sec Ops  approach to system design.

Nice-to-Have Skills

Hands-on usage of advanced AWS ecosystem tools:  ECR, Lambda, Bedrock, Cloud Watch, 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  Lang Chain or Llama Index .

Experience driving agile delivery models using Scrum, Kanban, or SAFe across multiple teams.