AIOps Engineer

DCG is a recruitment company focused on partnering with experienced professionals and providing continuous support. The role is part of ongoing recruitment projects to serve business needs.

• 5+ years of experience in DevOps, Site Reliability Engineering (SRE), Platform Engineering, or Infrastructure Operations
• At least 2 years of hands-on experience supporting MLOps, AI Infrastructure, or Large Language Model (LLM) platforms
• Strong experience with Kubernetes and OpenShift administration in production environments
• Proven experience deploying and operating vLLM-based inference platforms in production
• Strong understanding of LLM serving concepts including Paged Attention, continuous batching, and inference optimization techniques
• Deep knowledge of NVIDIA GPU technologies, CUDA drivers, NVIDIA Container Toolkit, and GPU troubleshooting
• Hands-on experience with Prometheus, Grafana, OpenTelemetry, and ELK Stack
• Experience building observability solutions with custom metrics, exporters, dashboards, and alerting
• Strong Python programming skills for automation and operational tooling
• Experience with Bash scripting and Linux systems administration
• Familiarity with GitLab CI, Jenkins, ArgoCD, and Infrastructure-as-Code practices
• Strong analytical and problem-solving skills for complex distributed environments
• Excellent communication and collaboration skills

• Design, deploy, and maintain vLLM inference services on OpenShift/Kubernetes running on bare-metal GPU infrastructure
• Manage NVIDIA GPU resources, including GPU partitioning and allocation
• Automate model lifecycle management, including model onboarding, versioning, deployment, hot-swapping, and rollback from private registries
• Implement and manage Horizontal Pod Autoscaling (HPA) based on workload demand, queue depth, and GPU utilization
• Optimize vLLM configurations and serving parameters for performance and efficiency
• Build and maintain observability and monitoring solutions including metrics collection, logging, and tracing
• Instrument vLLM endpoints with metrics for token consumption, latency, throughput, and error rates
• Develop usage tracking mechanisms for quota management and chargeback/showback
• Create and maintain Grafana dashboards for infrastructure, GPU utilization, inference performance, and business metrics
• Configure proactive monitoring and alerting using Prometheus and Alertmanager
• Implement and maintain API Gateway solutions for authentication, authorization, rate limiting, and routing
• Ensure secure operation through network segmentation, ingress/egress controls, and adherence to security best practices
• Maintain audit logging to support compliance and operational governance
• Collaborate with AI Engineering, Platform Engineering, Security, and Infrastructure teams