Dev Ops AWS (or Senior Cloud / Platform Engineer)

Company Description

MarkiTech.AI, a Canadian-based company, specializes in developing innovative digital healthcare solutions, AI agents, and automation systems for healthcare and telecommunications. Over the past decade, the company has successfully delivered 50+ global projects and introduced a range of advanced platforms, such as CliniScripts, YourDoctors.Online, SenSights.AI, and others aimed at improving care delivery and enhancing user experiences. With a focus on intelligent, workflow-integrated systems, MarkiTech.AI is poised to shape the future of AI in healthcare and telecommunications through cutting-edge automation and digital transformation. By prioritizing smarter and more efficient decision-making, MarkiTech.AI strives to create positive change across industries.

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Job title (MUST BE IN CANADA)

Senior DevOps Engineer (alternate: Senior Cloud / Platform Engineer)

About the role

We are hiring a senior DevOps engineer to own and evolve our cloud platform on

AWS, grounded in infrastructure as code, secure multi-account patterns, and

reliable delivery. You will shape the DevOps roadmap (standards, tooling,

automation, and operational excellence), support application releases, and

provide production support for critical workloads.

Amazon EKS is central to how we run workloads—we need someone with deep,

production-grade EKS expertise who has built and owned Kubernetes on AWS

end-to-end, not only deployed apps to a cluster someone else runs.

You will also lead how we adopt AI for infrastructure and platform work—not as

a buzzword, but as a practical force multiplier: safe use of AI-assisted authoring

and review for IaC and automation, clearer runbooks and incident workflows, and

evaluation of tools and patterns that improve speed without weakening security,

compliance, or change control. This role suits someone who combines deep AWS

practice with leadership: you can define “how we build and run” while still being

hands-on in pipelines, clusters, and incidents.

What you will do

Roadmap & standards: Define and socialize DevOps priorities (security,

reliability, cost, velocity). Align teams on AWS Well-Architected

practices, tagging, guardrails, and repeatable patterns for networking,

identity, secrets, and data.

AI adoption for infra & platform: Drive a pragmatic AI strategy for the

team—e.g. standards for AI-assisted IaC and pipeline changes (review

gates, testing, drift detection), documentation and runbook quality,

incident summarization and triage workflows where appropriate, and

guardrails so AI tooling fits regulated or high-stakes environments. Stay

current on vendor and open-source options; pilot, measure, and roll out

what actually reduces toil.

Infrastructure as code: Design, review, and implement changes using

Terraform and Terragrunt, with clear module boundaries, environmentspecific

config, and safe promotion across dev → non-prod →

production.

EKS (critical): Build, operate, and own the Kubernetes platform on AWS

—cluster lifecycle (creation, upgrades, patching), node groups / capacity,

networking (CNI, service mesh or ingress as used), security (RBAC,

admission controls, pod security, secrets and IRSA), add-ons, and cost/ reliability tuning. Partner with app teams on standards for workloads, namespaces, and safe rollouts; be the escalation point for cluster-level incidents.

Broader AWS platform: Operate and improve adjacent services—e.g.

RDS/Aurora, DynamoDB, object storage and CDN, KMS, Secrets

Manager, SNS (alerting), Lambda, EventBridge, and CI/CD

(CodePipeline / CodeBuild, connections to source control)—plus IAM,

VPC, and multi-tenant or multi-namespace patterns where applicable.

Release engineering: Partner with development teams on release

processes, deployment strategies, change management, rollbacks, and

post-release verification in regulated or high-stakes environments (e.g.

healthcare-adjacent workloads).

Production support: Participate in on-call or escalation rotation as

defined by the team; troubleshoot incidents, drive root-cause analysis,

and implement preventive fixes (runbooks, dashboards, alarms,

automation).

Observability & operations: Improve monitoring, logging, tracing, and

alerting; tune thresholds; reduce noise; document operational

procedures.

Collaboration: Work with security, architecture, and engineering leads to

implement least-privilege access, encryption, backup/DR posture, and

audit-friendly operations—including how AI-assisted workflows meet

security and audit expectations.

What we are looking for

Required

AWS

6+ years in software/systems / DevOps / SRE roles, including 4+ years

focused on AWS in production.

Strong command of infrastructure as code (Terraform) and modular,

environment-driven layouts (experience with Terragrunt or similar

composition patterns is a plus).

Deep, mandatory expertise in Amazon EKS: You have prior experience

building and owning Kubernetes on AWS—not only deploying

applications to a shared cluster. We expect fluency across the stack:

cluster design and lifecycle, upgrades and patching, networking

(VPC/CNI, DNS, ingress), identity and security (RBAC, IRSA, secrets,

guardrails), observability, capacity and performance, and production

troubleshooting. Surface-level or “I’ve used kubectl” experience is not

sufficient.

Solid grasp of CI/CD, artifact promotion, secrets injection, and safe

change practices in multi-environment pipelines.

Experience with production incidents: triage, communication, RCAs, and

durable remediation.

Demonstrated interest or experience in applying AI to DevOps/platform

work (e.g. AI-assisted coding and review workflows for IaC, internal

tooling, or operational documentation)—with judgment about limits,

verification, and risk in production systems.

Ability to influence without authority: written standards, design

reviews, and roadmap proposals that engineering teams actually adopt.

Excellent communication skills; comfortable working with distributed

teams and stakeholders outside pure engineering.

Preferred

AWS certifications (e.g. Solutions Architect Professional, DevOps

Engineer) or equivalent demonstrated depth.

Kubernetes certifications (e.g. CKA, CKS) or equivalent evidence of

advanced Kubernetes/EKS depth.

Experience with Helm / Helmfile, policy-as-code, or cluster baseline

tooling.

Familiarity with PostgreSQL/RDS, multi-tenant data patterns, or

regulated-industry constraints.

Experience shaping SLOs, error budgets, or platform KPIs.

Exposure to cost optimization (rightsizing, scheduling non-prod,

storage lifecycle) and FinOps collaboration.

Hands-on experimentation with AI coding assistants, internal LLM or

RAG patterns for ops knowledge, or evaluating vendor tools for the

platform team.