{"id":2000329,"url":"https://alion.io/job/radianarc-senior-observability-telemetry-engineer-radian-arc","title":"Senior Observability & Telemetry Engineer - Radian Arc","company":{"id":3908068,"name":"Radianarc","domain":"radianarc.com","url":"https://alion.io/company/radianarc","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-24T06:02:35Z"}},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":73000,"max_usd":201000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":3299},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"Edge AI","optional":false},{"name":"HPC","optional":false},{"name":"Incident Management","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"NCCL","optional":false},{"name":"NVLink","optional":false},{"name":"Python","optional":false},{"name":"Rust","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Time Series","optional":false},{"name":"ClickHouse","optional":true},{"name":"Grafana","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-04-24T15:07:19Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-24T18:51:34Z","board_verified":true,"closed_at":null,"days_open":183,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":182},"description":"Location & work modality: EMEA (remote)\nStart: ASAP\nType of Contract: Permanent, full-time\n\nAbout Radian Arc\nRadian Arc, now part of InferX, Submer's AI cloud and GPU infrastructure platform, provides an infrastructure-as-a-service (IaaS) platform for running cloud gaming, artificial intelligence and machine learning applications inside telecommunication carrier networks. Our teams across the USA, Australia, Central Europe, Malaysia, Singapore and Japan offer telecom operators a GPU-based edge computing platform without the need for capital expenditure, facilitating low latency and improved economics for value-added services and the monetization of 5G investments.\nWhat impact you will have\nMission: Design and build the observability platform that powers visibility, reliability, and performance insights for large-scale GPU cloud infrastructure as well as smaller edge deployments.\nThis role is responsible for designing and implementing key parts of the observability architecture across the platform, enabling engineering, operations, and customers to understand system behavior in real time across distributed AI workloads, GPU clusters, networking fabrics, storage systems, and edge inference environments.\nYou will design and operate low-latency, high-scale telemetry pipelines that collect, process, and analyze metrics, logs, and traces from infrastructure running across core datacenter clusters and smaller edge deployments. The platform you build will support internal operations, automated reliability mechanisms, and customer-facing observability experiences.\nAs a senior engineer, you will lead delivery of major observability initiatives, contribute to the evolution of telemetry standards and SLO implementation, and work with other teams to ensure observability is effectively integrated into the platform architecture from infrastructure to application layers.\nYou will collaborate closely with infrastructure, networking, storage, and platform engineering teams to provide clear visibility into performance bottlenecks, infrastructure degradation, and distributed workload behavior across both hyperscale GPU environments and smaller edge installations.\nThis role contributes directly to improving platform reliability by analyzing production telemetry, identifying systemic issues, and driving improvements in performance, efficiency, and operational stability across the stack.\nWhat you’ll do\nObservability Platform Architecture\nDesign and implement scalable telemetry pipelines for metrics, logs, and traces across distributed GPU infrastructure.\n\nArchitect observability systems capable of ingesting high-cardinality telemetry from thousands of nodes and services.\n\nBuild and operate telemetry storage systems optimized for large-scale time-series and event data.\n\nContribute to observability standards across services, including metrics, tracing instrumentation, logging, and SLO implementation.\n\nInfrastructure and Platform Observability\nBuild visibility across compute, storage, and networking layers of the platform.\n\nInstrument GPU clusters, inference workloads, and distributed training environments.\n\nDetect infrastructure degradation such as:\n\nGPU throttling,\n\nNetwork congestion,\n\nStorage latency,\n\nHardware degradation.\n\nImplement telemetry pipelines for GPU, CPU, network, and storage performance metrics.\n\nCustomer-Facing Observability\nBuild dashboards and monitoring tools that expose system health and performance to both internal teams and customers.\n\nProvide insights into workload performance including:\n\nGPU utilization,\n\nStorage throughput,\n\nNetwork latency,\n\nDistributed inference performance.\n\nDevelop performance analysis tools that help customers understand system bottlenecks.\n\nNetwork and Infrastructure Telemetry\nDevelop and maintain network observability platforms.\n\nBuild telemetry collectors and exporters using Python or Go.\n\nIngest telemetry from infrastructure components including:\n\nNVIDIA Cumulus Linux,\n\nVyOS routers,\n\nCitrix NetScaler / WAF.\n\nDesign telemetry ingestion pipelines using protocols such as:\n\ngNMI,\n\nSNMP,\n\nStreaming telemetry.\n\nReliability Engineering\nDesign advanced alerting and anomaly detection systems.\n\nContribute to platform SLOs, SLIs, and reliability metrics.\n\nBuild automated detection of infrastructure anomalies.\n\nIntegrate observability signals with operational workflows and incident management systems.\n\nParticipate in on-call rotations supporting platform observability and telemetry infrastructure.\n\nCross-Team Collaboration\nPartner with platform, networking, storage, and compute teams to instrument services.\n\nWork closely with operations teams to improve monitoring and incident response.\n\nProvide guidance and mentorship to engineers on observability best practices.\n\nPromote good observability practices across teams and help engineers adopt effective instrumentation and monitoring patterns.\n\nTechnical Stack: Observability and telemetry technologies used across the platform include:\nObservability Framework\nPrometheus.\n\nOpenTelemetry.\n\nGrafana.\n\nDistributed logging systems.\n\nHigh-scale telemetry databases, such as ClickHouse or similar.\n\nHardware and Infrastructure Telemetry\nRedfish / BMC telemetry.\n\nIPMI.\n\nLinux system metrics.\n\nHardware health monitoring and node lifecycle telemetry.\n\nNVIDIA GPU Telemetry\nNVIDIA DCGM.\n\nDCGM Exporter.\n\nNVML.\n\nNVIDIA GPU Operator telemetry stack.\n\nNVSwitch / NVLink telemetry.\n\nAI Workload Telemetry\nDistributed training telemetry.\n\nInference latency and throughput metrics.\n\nNCCL communication health.\n\nGPU synchronization latency.\n\nKV-cache access latency for inference workloads.\n\nDataset loading and storage I/O performance.\n\nNetworking Telemetry\nNVIDIA NetQ.\n\ngNMI streaming telemetry.\n\nSNMP.\n\nNetwork flow telemetry.\n\nRDMA / RoCE performance monitoring.\n\nWhat you’ll need\nRequired Experience\nProven experience operating large distributed infrastructure platforms.\n\nStrong background in observability systems and telemetry pipelines.\n\nExperience building metrics, logging, tracing, alerting, and dashboards at production scale.\n\nStrong programming skills in Go, Python, or Rust.\n\nExperience with large-scale time-series data platforms.\n\nExperience with large-scale GPU cloud platforms, HPC environments, or AI infrastructure.\n\nExperience monitoring AI workloads such as training or inference clusters.\n\nInfrastructure Knowledge\nDeep understanding of distributed systems observability.\n\nFamiliarity with cloud-native infrastructure such as Kubernetes, automation, and CI/CD.\n\nExperience operating observability systems for high-performance or large-scale environments.\n\nNetworking and Infrastructure Telemetry\nExperience monitoring complex networking environments.\n\nFamiliarity with telemetry protocols such as gNMI, SNMP, and streaming telemetry.\n\nExperience integrating network and system telemetry into centralized monitoring platforms.\n\nAnalytical Skills\nStrong data analysis capabilities.\n\nAbility to interpret complex telemetry signals and translate them into actionable insights.\n\nAbility to diagnose performance issues across distributed systems.\n\nWhat we offer\nAttractive compensation package reflecting your expertise and experience.\n\nA great work environment characterised by friendliness, international diversity, flexibility, and a hybrid-friendly approach.\n\nYou'll be part of a fast-growing scale-up with a mission to make a positive impact, offering an exciting career evolution.\n\nOur job titles may span more than one job level. The actual base pay is dependent on a number of factors, such as transferable skills, work experience, business needs and market demands.\nOur Inclusive Responsibility\nRadian Arc is committed to creating a diverse and inclusive environment and is proud to be an equal opportunity employer. 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