> Markdown version of [/jobs/ext/4002853-senior-ai-infrastructure-engineer](https://www.wearedevelopers.com/jobs/ext/4002853-senior-ai-infrastructure-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Infrastructure Engineer - **Company:** Peoplentech Llc - **Location:** Alexandria, VA, United States - **Experience:** Expert - **Salary:** $156,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Microsoft Azure, Computer Clusters, Continuous Integration, Linux, Machine Learning, OpenShift, Performance Tuning, Azure Machine Learning, AI Infrastructure, Grafana, Multi-Cloud, Generative AI, AI Platforms, Kubernetes, Machine Learning Operations, Splunk, AIOps, Serverless Computing - **Published:** October 29, 2026 - **Apply:** https://www.thejobnetwork.com/job/5f286067-41d0-4424-abc1-69144cb78f99/senior-ai-infrastructure-engineer ## About the Role 5 years of infrastructure engineering experience. - 5 years troubleshooting complex end-to-end architectures(including CI/CD pipeline). - 5 years Linux systems experience. - 4 years supporting AI/ML platforms. - 4 years of Kubernetes / container platform experience including production support. Desired Qualifications - Experience with Generative AI and Predictive AI platforms. - Hands-on GPU platform operations including scheduling, quota, and performance tuning. - Experience with OpenShift in GPU-enabled, multi-tenant environments. - Experience designing or operating GPU Super Pods. - Deep experience with observability using Grafana, Splunk, and custom telemetry pipelines. - Experience building AI- or agent-driven automation tooling (AIOps). - Hands-on experience supporting AI/ML workloads on GCP and Azure, including GPU-backed services and managed AI infrastructure - Experience operating hybrid or multi-cloud AI platforms, with an understanding of cloud-native services, networking, identity, and cost optimization for Generative and Predictive AI - Strong monitoring of AI signals such as inference latency and GPU utilization. - Experience with BCP/DR, resiliency, and highly available architectures. ## Description Lead complex infrastructure initiatives supporting Generative AI and Predictive AI platforms from design to production operations. - Serve as a technical lead for platforms supporting AI/ML model training, inference, and batch workloads. - Design, build, deploy, and operate OpenShift-based container platforms optimized for high-performance GPU workloads. - Build, support and operate scalable GPU SuperPod architecture with large multi-node GPU clusters. - Own monitoring, alerting, and observability using Grafana, Splunk, and enterprise telemetry tools. - Define SLIs/SLOs and build actionable alerts to proactively detect performance, capacity, and resiliency risks. - Build AI- and agent-based automation tools for self-healing, scaling, diagnostics, and incident remediation. - Apply AIOps techniques to reduce alert fatigue and improve platform reliability. - Lead production incident analysis and ensure operational rigor and root-cause prevention. - Mentor engineers and influence stakeholders across a geographically distributed organization.