Observability Platform Engineer — Neocloud
Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Aug 31, 2026. Mirantis scores B on the Alion truth index.
About Mirantis
Mirantis is the Kubernetes-native AI infrastructure company, enabling organizations to build and operate scalable, secure, and sovereign infrastructure for modern AI, machine learning, and data-intensive applications. By combining open source innovation with deep expertise in Kubernetes orchestration, Mirantis empowers platform engineering teams to deliver composable, production-ready developer platforms across any environment-on-premises, in the cloud, at the edge, or in sovereign data centers. As enterprises navigate the growing complexity of AI-driven workloads, Mirantis delivers the automation, GPU orchestration, and policy-driven control needed to manage infrastructure with confidence and agility. Committed to open standards and freedom from lock-in, Mirantis ensures that customers retain full control of their infrastructure strategy.
Mirantis serves many of the world’s leading enterprises, including Adobe, DocuSign, Liberty Mutual, PayPal, Reliance Jio, Societe Generale, Splunk, and Volkswagen. Learn more at www.mirantis.com.
About the Role
Mirantis is building out our Neocloud service offering - managing large-scale infrastructure to a high SLA for customers running demanding compute workloads. As that offering scales, so does the volume and complexity of telemetry we need to collect, correlate, and act on. We're looking for an Observability Platform Engineer to design and build the monitoring, logging, tracing, and alerting platform that our operations teams depend on to detect and resolve incidents fast - at large scale, across a globally distributed environment.
This is a hands-on, build-it role. You'll be the person who turns "we have no visibility into this" into a platform that surfaces the right signal at the right time, and turns "we found out from the customer" into "we caught it before they noticed."
Key Responsibilities
Design, build, and operate observability platform components - metrics, logging, distributed tracing, and alerting - for large-scale infrastructure environments
Build telemetry pipelines capable of handling high cardinality, high volume data from large fleets of infrastructure, with an eye on cost, retention, and query performance
Define and implement SLO/SLI frameworks and alerting strategies that reduce noise and surface real signal to on-call engineers
Partner closely with service delivery and operations teams to understand what they need to see during an incident, and build for that - not just for dashboards nobody opens
Integrate observability tooling with incident management workflows, including root-cause analysis support and post-incident review data
Continuously improve detection speed and reduce mean-time-to-detect (MTTD) and mean-time-to-resolve (MTTR) across the platform
Contribute to the roadmap for AI-assisted operations tooling (e.g., automated triage, anomaly detection, engineer-assist tooling) as it matures
Own the reliability, scalability, and security of the observability stack itself - it needs to be up when everything else is on fire
Document architecture, runbooks, and operational practices so the platform is maintainable beyond you
What Success Looks Like
Operations teams can diagnose incidents faster because the right data is surfaced automatically, not hunted for manually
Alert volume is high-signal, low-noise - engineers trust what fires
The observability platform scales cleanly as infrastructure footprint grows, without cost or performance surprises
Reduced MTTD/MTTR trends, tracked and demonstrable over time
A platform other engineers actually want to build on, not work around
Proven experience designing and building observability platforms for large-scale, production infrastructure environments (not just consuming an existing setup - actually building one)
Strong hands-on experience with metrics, logging, and distributed tracing tooling (e.g., Prometheus, Grafana, OpenTelemetry, Loki, Thanos/Cortex/Mimir, Elasticsearch/OpenSearch, Jaeger/Tempo, or equivalents)
Experience with high-volume telemetry pipelines and the tradeoffs involved (cardinality, retention, cost, query latency)
Strong software engineering skills in at least one language commonly used in this space (e.g., Go, Python, Rust)
Experience with Kubernetes and cloud-native infrastructure
Solid understanding of SLO/SLI/error-budget practices and alerting design that minimizes noise
Comfortable working in a fast-moving environment where the platform is being built out alongside the infrastructure it's monitoring
Strong communication skills - able to work directly with operations/service delivery teams to understand real incident-response needs, not just technical specs
Preferred Skills & Experience
Experience building observability for GPU/HPC infrastructure or other specialized, high-performance compute environments
Experience with eBPF-based observability tooling
Familiarity with AIOps/ML-based anomaly detection or automated triage systems
Experience operating in a managed services or MSP context, where observability directly drives customer-facing SLAs
Contributions to open-source observability projects
What does Mirantis offer you?
- Work with an established Silicon Valley leader in the cloud infrastructure industry;
- Work with exceptionally passionate, talented and engaging colleagues, helping Fortune 500 and Global 2000 customers implement next-generation cloud technologies;
- Be a part of cutting-edge, open-source innovation;
- Thrive in the high-energy environment of a young company where openness, collaboration, risk-taking, and continuous growth are valued;
- Professional development and training;
- Attend conferences and working groups;
- Company outings, happy hours, hackathons, and tech talks;
- Receive a competitive compensation package with a strong benefits plan.
It is understood that Mirantis, Inc. may use automated decision-making technology (ADMT) for specific employment-related decisions. Opting out of ADMT use is requested for decisions about evaluation and review connected with the specific employment decision for the position applied for. You also have the right to appeal any decisions made by ADMT by sending your request to [email protected]
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