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Senior Infrastructure/ DevOps Engineer, Fintech

Lazer
CompanyLazer
CategoryEngineering
LocationCanada
RemoteRemote
EmploymentNot stated
LevelSenior
SalaryNot stated by the employer
Posted15 Jun 2026
Last verified3 Aug 2026
SourceEmployer career page (ashby)
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Description
Lazer https://www.lazertechnologies.com is a world-class digital product studio composed of 180+ senior engineers and designers with backgrounds from companies like Apple, Google, Coinbase, and more. With our product experience, we have designed, engineered, and grown products from $0 to $200M in revenue. Clients seek out our help because we have the talent to deeply understand their needs and provide industry, technical, or product insights that are uniquely valuable to their efforts. Our clients range from early-stage startups and venture studios to recognizable retail brands and exciting enterprises. Some of our notable clients include Google, Shopify, Coinbase, Alchemy, Hinge, OVO, Polymarket, and more. We are a remote-first organization headquartered in Toronto, Ontario, with employees worldwide. We believe in providing the best experience possible for all Lazerites by fostering a strong community through regular events, company vacations, competitive compensation, unlimited PTO, and more! Join Lazer and help us solve problems and build the next generation of products! WHO YOU ARE: - Seniority: Minimum of 5 years dedicated experience in DevOps, Infrastructure, or SRE roles. Expert tooling: expert with Docker, Kubernetes (k8s), and Terraform/Pulumi. - Cloud proficiency: Deep, proven expertise in either AWS or GCP infrastructure, with the ability to quickly grasp and transition to other cloud providers. - Development skills: Strong ability to write clean, maintainable code for automation in Go, Python, or Node.js. - Security focus: Demonstrable experience implementing and maintaining modern cloud security controls and meeting key compliance standards (SOC 2, PIPEDA, HIPAA, and/or GDPR). - Independent, proactive, and cross-functional: Proven ability to quickly onboard, diagnose problems, and propose and implement solutions with minimal oversight. Experienced in a consultant or freelancer capacity, with the ability to understand and communicate effectively with both technical and non-technical stakeholders. WHAT YOU'LL DO: - Infrastructure as Code (IaC): Quickly implement and adapt infrastructure using Terraform, Pulumi, or other major IaC tools. - Containers: Docker is critical. Deeply understand how to design, build, and optimize secure, multi-stage Dockerfiles. - CI/CD: Design, build, and manage robust CI/CD pipelines to automate testing, building, and deployment across environments. - Core cloud services (AWS or GCP): Provision and manage foundational services. Deep expertise in one major provider is required, transferable to the other. - Container compute: Expertise in at least one major container platform: EKS, GKE, ECS, Fargate, or Cloud Run. (Kubernetes is highly valued, particularly EKS or GKE.) - Networking: Know when to use load balancers, VPNs for secure connectivity, and private VPCs for isolation. Apply subnetting, routing, VPC peering, and NAT gateways to build secure systems. - Storage: S3 (AWS) or Cloud Storage (GCP). - Databases: RDS (AWS) or CloudSQL (GCP). - Serverless: Deploy event-driven components using AWS Lambda, GCP Cloud Functions, or equivalents. - CDNs and message queues. - Security: Protect PII; apply encryption, secrets management, network firewalls, and web application firewalls (AWS WAF, GCP Cloud Armor) following security best practices. - Automation and scripting: Write high-quality automation and tooling in Go, Python, Node.js, or Bash for client-specific operational challenges. - Monitoring and operations: Ensure robust monitoring and high system uptime. NICE TO HAVE: The following would be a bonus experience to have, though highlight any additional experience or skills you may have. We like working with people with varied backgrounds and experiences. - Production AI/agent experience: Hands-on experience running LLM or agent systems in production, including how they fail differently from deterministic services: nondeterministic outputs
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