Note: The job is a remote job and is open to candidates in USA. Systematix is a global consulting and resourcing firm delivering technology solutions to clients across North America and the United Kingdom. They are seeking a Senior Site Reliability Engineer to establish reliability, observability, automation, and operational engineering capabilities for an enterprise AI and Data platform, working with Platform Engineering, DevOps, and MLOps teams.
Responsibilities
- Design and implement monitoring, observability, and alerting capabilities across Microsoft Azure cloud infrastructure and AI/ML environments
- Establish Site Reliability Engineering practices, standards, operating models, and engineering principles
- Define and implement Service Level Indicators (SLIs), Service Level Objectives (SLOs), and appropriate platform reliability metrics
- Build dashboards, telemetry, and actionable alerting that provide meaningful visibility into platform health and performance
- Implement observability solutions using technologies such as Prometheus, Grafana, OpenTelemetry, or comparable platforms
- Automate operational processes, remediation, recovery, and repetitive support activities wherever possible
- Improve platform availability, scalability, resilience, performance, and recoverability
- Develop proactive infrastructure health, capacity, and performance monitoring capabilities
- Support the reliability of production AI/ML applications, services, and GPU-intensive workloads
- Partner with Platform Engineering teams to incorporate reliability and resilience into cloud infrastructure architecture
- Partner with DevOps engineers to improve deployment reliability and automate operational processes
- Partner with MLOps engineers to establish monitoring and observability for model, inference, and supporting infrastructure
- Develop incident response processes, runbooks, operational tooling, and automated recovery capabilities
- Support capacity planning and performance management across cloud and AI infrastructure
- Lead and contribute to root-cause analysis and post-incident reviews
- Identify recurring operational issues and develop engineering solutions that eliminate or significantly reduce manual intervention
- Drive continuous improvement of platform reliability, observability, and operational efficiency
Skills
- No H-1B sponsorship or any visa sponsorship available
- • Extensive senior-level experience in Site Reliability Engineering, Production Engineering, Systems Engineering, or a closely related engineering discipline
- • Strong hands-on experience with Microsoft Azure and enterprise cloud infrastructure
- • Deep expertise in monitoring, observability, telemetry, and alerting
- • Hands-on experience with Prometheus, Grafana, OpenTelemetry, or comparable observability technologies
- • Demonstrated experience defining and implementing SLIs, SLOs, reliability metrics, and actionable alerting
- • Strong Infrastructure as Code experience
- • Advanced automation and scripting capabilities
- • Strong experience with Docker, Kubernetes, and containerized production environments
- • Experience with CI/CD pipelines and modern software delivery practices
- • Demonstrated experience operating and improving complex production systems at enterprise scale
- • Experience automating operational processes, remediation, and recovery activities
- • Strong understanding of distributed systems, cloud architecture, scalability, resilience, and performance
- • Exceptional troubleshooting, root-cause analysis, and systems-thinking capabilities
- • Strong software engineering mindset with a demonstrated preference for solving operational problems through engineering and automation rather than manual processes
- • Strong communication and collaboration skills with the ability to work across Platform Engineering, DevOps, MLOps, cloud, security, data, and application engineering teams
- The successful candidate must be based within, or able to consistently work, Eastern Time Zone business hours
- • Experience supporting AI and machine learning infrastructure in production environments
- • Experience supporting GPU, accelerated computing, or high-performance computing environments
- • Strong Python development or scripting experience
- • Experience with Azure Machine Learning and related Azure AI/data services
- • Experience with MLOps platforms and machine learning lifecycle infrastructure
- • Experience implementing observability for machine learning models, inference services, and supporting infrastructure
- • Experience establishing an SRE capability, standards, or operating model within an immature or evolving engineering environment
- • Experience working within large, global, or federated enterprise environments
Benefits
- Remote contract opportunity supporting a strategic enterprise AI, Data, and Cloud engineering initiative
Company Overview