AIOps Engineer

DCG Remote Warszawa 2026-10-14

Requirements:

  • 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, including custom metrics, exporters, dashboards, and alerting mechanisms 
  • Strong Python programming skills with experience developing 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 with the ability to work in complex, distributed environments 
  • Excellent communication and collaboration skills 

As a recruitment company, DCG understands that every business is powered by experienced professionals. Our management style and partnership approach enable us to meet your needs and provide continuous support. Due to our ongoing growth and the large number of recruitment projects we undertake for our partners, we are currently looking for:

AIOps Engineer


Offer:

  • Private medical care
  • Co-financing for the sports card
  • Training & learning opportunities
  • Constant support of dedicated consultant
  • Employee referral program
,[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, to maximize utilization across multiple models and tenants, Automate model lifecycle management, including model onboarding, versioning, deployment, hot-swapping, and rollback from private registries such as Hugging Face Enterprise and S3, Implement and manage Horizontal Pod Autoscaling (HPA) based on workload demand, queue depth, and GPU resource utilization.Optimize vLLM configurations and serving parameters to maximize performance, throughput, and resource efficiency, Build and maintain observability and monitoring solutions for AI inference services, including metrics collection, logging, and tracing, Instrument vLLM endpoints to expose metrics related to token consumption, latency, throughput, and error rates, Develop usage tracking mechanisms to monitor token consumption by API key, user, team, or department, supporting quota management and chargeback/showback requirements, Create and maintain Grafana dashboards covering infrastructure health, GPU utilization, inference performance, service availability, and business consumption metrics, Configure proactive monitoring and alerting using Prometheus and Alertmanager to detect infrastructure failures, performance degradation, and unusual consumption patterns, Implement and maintain API Gateway solutions to provide authentication, authorization, rate limiting, and intelligent routing to inference services, Ensure secure operation of AI services through network segmentation, ingress and egress controls, and adherence to security best practices, Maintain audit logging capabilities to support compliance, security investigations, and operational governance, Collaborate with AI Engineering, Platform Engineering, Security, and Infrastructure teams to deliver reliable, scalable, and secure AI services, Participate in troublesho] Requirements: OpenShift, Kubernetes, GPU, AI, MLOps, LLM, Python, Linux, Azure Additionally: Sport Subscription, Private healthcare, Employee referral bonus.
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