Senior Site Reliability Engineer

Role & responsibilities

Key responsibilities

• Build resilient, self-healing systems that scale seamlessly and improve system reliability.
• Monitor system health using charts, graphs, and logs; detect, trace, and respond to issues at scale.
• Write post-mortems and lead root cause analysis to implement corrective measures preventing issues from reoccurring.
• Create, modify, and document risk-mitigation strategies to eliminate potential risks that could impact performance, scalability, and reliability.
• Create, modify, and maintain CI/CD pipeline scripts and configurations across multiple domains.
• Create and evolve processes for source control, build, integration, automated testing, security scanning, and application delivery.
• Work with Product Engineering and Implementation Services to deliver end-to-end software delivery pipelines.
• Create automated development and operations scripts to ensure reliability, scalability, and repeatability of pipelines.
• Leverage Infrastructure as Code and Configuration as Code to automate deployments.
• Use AI coding assistants to accelerate authoring of Infrastructure as Code configurations, CI/CD pipeline scripts, and operational runbooks.
• Define and champion AI-first reliability practices across the SRE function, including standards for AI-assisted log triage, IaC generation, and post-mortem analysis workflows.
• Provide mentoring and technical guidance to Site Reliability Engineers.
• Participate in an on-call rotation.
  Mandatory AI Engineering Competencies
  Candidates must demonstrate the ability to work effectively in an AI-First Software Development Lifecycle.
• Proficient in AI-assisted software development using modern coding assistants to accelerate design, implementation, testing and documentation.
• Ability to translate business requirements into structured specifications prior to implementation (Spec-Driven Development).
• Strong prompt engineering skills for software engineering tasks including code generation, test generation, debugging, documentation and architecture.
• Ability to critically evaluate AI-generated outputs for correctness, security, performance and maintainability.
• Experience using AI to generate and maintain automated unit, API and integration tests.
• Demonstrated ability to work with AI agents while retaining full engineering ownership and accountability.

Essential Qualifications & Experience
You must have all of the following:

• Significant experience with GitHub or similar source repository and CI/CD collaboration platforms.
• Significant experience with container orchestration platforms such as Kubernetes or EKS.
• Significant experience with Docker or similar container technology.
• Significant experience with Python-based applications and Bash scripting.
• Significant experience with PostgreSQL.
• Solid understanding of well-architected cloud infrastructure in AWS, including: compute (EKS, EC2), storage (EFS, EBS), database (PostgreSQL), and networking (VPC, subnets, CIDRs, NACLs, security groups, VPN, Transit Gateway).
• Demonstrated ability to mentor and guide other engineers.
• Fluent written and spoken English.

Desired Qualifications & Experience
Ideally, you'll have some of the following:

• Experience with cloud computing and hybrid on-premises solutions.
• AWS Certification (Developer, DevOps, Architect, or similar).
• Experience with Ansible, Chef, Puppet, or similar configuration management technology.
• Experience with ArgoCD or similar continuous delivery technology.
• Experience with Terraform or similar infrastructure as code tooling.
• Experience in the telecommunications or utilities industries