AIOps Engineer

DCG; Województwo mazowieckie; On-site; PLN 180,000 - 240,000; Full time

Benefits offered by this job

Private medical care

Sports card subsidy

Training opportunities

Consultant support

Job summary

DCG is seeking an experienced DevOps/Platform Engineer to design, deploy, and manage vLLM-based AI inference services on OpenShift/Kubernetes with bare-metal GPUs. You will optimize GPU utilization, automate model lifecycle management, and build comprehensive observability across metrics, logs, and traces.

The role requires deep knowledge of NVIDIA GPUs, vLLM serving concepts, and strong Python/CI tooling. You will collaborate with multiple engineering teams to ensure secure, scalable AI

Qualifications

• 5+ years in DevOps, SRE, Platform Engineering, or Infrastructure Operations.
• At least 2 years hands-on experience supporting MLOps, AI Infra, or LLM platforms.
• Strong experience with Kubernetes and OpenShift administration in production.
• Proven experience deploying and operating vLLM-based inference platforms.
• 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.
• Strong Python programming skills with automation and tooling.
• Bash scripting and Linux systems administration.
• Familiarity with GitLab CI, Jenkins, ArgoCD, and Infrastructure-as-Code practices.
• Strong analytical and problem-solving skills; excellent communication and collaboration.

Responsibilities

• Design, deploy, and maintain vLLM inference services on OpenShift/Kubernetes on bare-metal GPU infrastructure.
• Manage NVIDIA GPU resources, including partitioning and allocation for multi-model use across tenants.
• Automate model lifecycle management: onboarding, versioning, deployment, hot-swapping, rollback from private registries.
• Implement and manage Horizontal Pod Autoscaling based on workload and GPU utilization.
• Build and maintain observability/monitoring for AI inference: metrics, logs, traces.
• Instrument vLLM endpoints to expose token, latency, throughput, and error-rate metrics.
• Develop usage tracking for token consumption by API key, user, team, or department (quotas, chargeback).
• Create Grafana dashboards for infrastructure health, GPU utilization, inference performance, and business metrics.
• Configure proactive monitoring/alerts with Prometheus and Alertmanager for failures and anomalies.
• Implement API Gateway for auth, rate limiting, and intelligent routing to inference services.
• Ensure secure operation via network segmentation, ingress/egress controls, and security best practices.
• Maintain audit logging for compliance, security investigations, and governance.
• Collaborate with AI Eng, Platform Eng, Security, and Infra teams to deliver reliable, scalable AI services.
• Participate in troubleshooting, incident response, root cause analysis, and platform improvements.

Skills

Kubernetes

OpenShift

vLLM

Python

Bash

Prometheus

Grafana

OpenTelemetry

ELK Stack

NVIDIA GPUs

GitLab CI

Jenkins

ArgoCD

IaC

Communication

Tools

GitLab CI

Jenkins

ArgoCD

Job description

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:

Responsibilities:

• 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 troubleshooting, incident response, root cause analysis, and continuous platform improvement initiatives

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

Offer:

• Private medical care
• Co‑financing for the sports card
• Training & learning opportunities
• Constant support of dedicated consultant