We’re looking for a hands-on DevOps Engineer to join our growing team. You’ll own the build, deployment and monitoring pipelines that power our services—from API backends to data-processing workflows. If you love automation, infrastructure as code, and keeping things running smoothly at scale, this is for you.

What You’ll Do
Containerization & Orchestration
Build, optimize and maintain Docker images and CI/CD pipelines
Design and manage container workflows on Linux hosts
Infrastructure as Code
Automate provisioning and configuration with Ansible
Collaborate on GitLab-CI pipelines for automated testing, builds and deploys
CI/CD Ownership
Write and maintain GitLab CI YAML jobs that cover linting, testing, security scans and production rollouts
Troubleshoot pipeline failures and optimize for speed and reliability
Monitoring & Observability
Deploy and configure Prometheus + Grafana dashboards to track service health
Set up Loki for centralized log aggregation and search
Integrate with the ELK stack (Elasticsearch, Logstash, Kibana) for deeper log analysis
Reliability & Scaling
Implement service-level objectives (SLOs) and automated alerts
Perform capacity planning, load testing and performance tuning
Collaboration & Security
Work closely with developers, QA and product teams to streamline releases
Enforce best practices around secrets management, access controls and patching

Required Qualifications
3+ years of experience in a DevOps or SRE role
Solid Linux administration skills (Ubuntu, Debian, etc.)
Proficient with Docker and container lifecycle management
Experienced in writing Ansible playbooks, roles and inventories
Comfortable building and maintaining GitLab CI pipelines
Hands-on with Prometheus/Grafana for metrics and alerting
Familiar with Loki and/or the ELK stack for log aggregation and analysis
Strong scripting skills (Bash, Python, or similar)
A self-starter who loves documentation, automation and continuous improvement

Nice to Have
Background in machine learning or data science workflows
Experience with MLOps frameworks (Kubeflow, MLflow, TFX, etc.)
Familiarity with Kubernetes or other container orchestration platforms
Cloud experience (AWS, GCP or Azure)