Senior DevOps Engineer - Healix

10/7/26

Apply now

• Start applying with LinkedIn
• Apply Now


Start


• Please wait...

Job Type: Permanent Work Model: Hybrid Reference code: 135131 Primary Location: Toronto, ON All Available Locations: Toronto, ON; Burlington, ON; Calgary, AB; Edmonton, AB; Fredericton, NB; Halifax, NS; Kitchener, ON; Moncton, NB; Ottawa, ON; Regina, SK; Saint John, NB; Saskatoon, SK; St. John's, NL; Winnipeg, MB

Summary

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.

What will your typical day look like?

You will 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.

Key Responsibilities:

• 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 12 non-prod 12 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.

About the team

Healix is an AI-native venture built inside Deloitte, creating a governed AI workflow layer that lifts the operational burden on hospital staff - without disrupting the systems they already rely on. Our platform sits across existing hospital infrastructure to deploy and scale AI-enabled workflows with standardized trust controls applied once, so hospitals can start with one use case and expand to many without restarting governance from scratch. The thesis is simple: do the work you already trust Deloitte with, just faster, smarter, and at scale.

We're backed by Deloitte, the world's largest professional services firm, which means we have real enterprise distribution, real health system access, and real institutional credibility from day one. That's not a small thing in healthcare - trust is the product, and we come pre-loaded with it. We're also early enough that the person we hire here will define what Healix's product org looks like for the next decade. This isn't a point solution play. Healix is being built as the foundational AI operating layer for hospital operations - the platform every future AI-enabled workflow runs on top of, inheriting the same security, auditability, and human-oversight controls without multiplying IT burden or compliance risk.

Enough about us, let's talk about you

You are someone who has these skills, experience & qualifications:

• 6+ years of experience in software engineering, systems engineering, DevOps, or Site Reliability Engineering (SRE), including at least 4 years working with AWS in production environments.
• Strong expertise in Infrastructure as Code (IaC) using Terraform, with experience designing modular, environment-driven infrastructure. Experience with Terragrunt or similar composition frameworks is considered an asset.
• Deep, hands-on expertise with Amazon EKS. This role requires direct experience building, operating, and owning Kubernetes platforms on AWS, not simply deploying applications to an existing cluster. You should be comfortable with:
  • Cluster architecture, lifecycle management, upgrades, and patching
  • Networking, including VPCs, CNI, DNS, and ingress
  • Security and identity management, including RBAC, IRSA, secrets management, and governance controls
  • Observability, monitoring, and troubleshooting
  • Capacity planning, performance optimization, and production support Experience limited to basic Kubernetes usage or application deployment is not sufficient.
• Strong understanding of CI/CD pipelines, artifact promotion strategies, secrets management, and safe deployment practices across multiple environments.
• Experience managing production incidents, including triage, stakeholder communication, root cause analysis (RCA), and implementing long-term corrective actions.
• Demonstrated interest in applying AI to platform engineering and DevOps workflows, such as AI-assisted infrastructure development, code reviews, operational tooling, or documentation. Candidates should understand the limitations, validation requirements, and risks associated with using AI in production environments.
• Proven ability to influence technical direction without direct authority through standards, architecture reviews, and roadmap recommendations that are successfully adopted by engineering teams.
• Excellent communication and collaboration skills, with the ability to work effectively across distributed teams and with stakeholders beyond engineering.

It would be great for you to have some of these nice to haves as well:

• AWS certifications such as AWS Solutions Architect Professional or AWS DevOps Engineer Professional, or equivalent practical experience demonstrating similar depth of expertise.
• Kubernetes certifications such as Certified Kubernetes Administrator (CKA) or Certified Kubernetes Security Specialist (CKS), or equivalent demonstrated experience managing Kubernetes and EKS platforms at scale.
• Experience with Helm, Helmfile, policy-as-code frameworks, or Kubernetes cluster baseline tooling.
• Familiarity with PostgreSQL, Amazon RDS, multi-tenant architectures, or technology environments subject to regulatory requirements.
• Experience defining and managing Service Level Objectives (SLOs), error budgets, and platform performance metrics.
• Exposure to cloud cost optimization and FinOps practices, including rightsizing resources, scheduling non-production workloads, and implementing storage lifecycle strategies.
• Hands-on experience evaluating or using AI coding assistants, internal LLM or RAG solutions for operational knowledge management, or AI tools that support platform engineering teams.

Total Rewards

The salary range for this position is $105,000 - $175,000, and individuals may be eligible to participate in our bonus program. Deloitte is fair and competitive when it comes to the salaries of our people. We regularly benchmark across a variety of positions, industries, sectors, targets, and levels. Our approach is grounded on recognizing people's unique strengths and contributions and rewarding the value that they deliver.

Our Total Rewards Package extends well beyond traditional compensation and benefit programs and is designed to recognize employee contributions, encourage personal wellness, and support firm growth. Along with a competitive base salary and variable pay opportunities, we offer a wide array of initiatives that differentiate us as a people-first organization. On top of our regular paid vacation days, some examples include: $4,000 per year for mental health support benefits, a $1,300 flexible benefit spending account, firm-wide closures known as "Deloitte Days", dedicated days of for learning (known as Development and Innovation Days), flexible work arrangements and a hybrid work structure.