We're looking for a DevOps Engineer to co-manage and strengthen our infrastructure, spanning on-premises servers, network security, and our cloud environment. Working alongside the engineering team, you'll help mature our platform through improved observability, automated deployments, and robust CI/CD practices.
The core focus of this role is DevOps fundamentals, including infrastructure, networking, and automation, with a growing footprint in MLOps as our machine learning workflows move toward production-grade tooling.
Responsibilities
Infrastructure & Security:
Co-manage server infrastructure: provisioning, hardening, patching, backups, and access management
Support firewall and network security operations: rule management, VPN access, segmentation, and anomaly monitoring
Administer cloud resources: services, IAM, cost monitoring, and security configuration
Automation & Tooling:
Design and implement CI/CD pipelines for automated testing and deployment
Introduce infrastructure-as-code to make environments reproducible and well-documented (Terraform, Ansible, or similar)
Establish observability across servers, network, and applications: metrics, logging, alerting, and dashboards
Reduce manual operational work through automation
MLOps:
Support ML workflows with pipeline automation, experiment tracking, and model deployment tooling
Containerize and serve models, with monitoring for model and data health
Contribute to establishing reproducible, versioned ML practices
Requirements
1+ years of hands-on experience in DevOps, systems administration, SRE, or infrastructure-focused roles
Working knowledge of networking and network security: firewalls, VPNs, DNS, TLS, ports/protocols, and hardening practices
Experience administering Linux servers (provisioning, users and permissions, services, troubleshooting)
Familiarity with at least one major cloud provider (AWS, GCP, or Azure)
Experience with containers (Docker) and scripting (Bash and/or Python)
Exposure to CI/CD concepts and tooling (GitHub Actions, GitLab CI, Jenkins, etc.)
Interest in MLOps and willingness to learn the ML lifecycle: training pipelines, model deployment, and monitoring
Strong ownership mindset and clear communication around security and reliability trade-offs
Nice to Have
Experience managing on-premises infrastructure (physical servers, local networking, hypervisors)
Hands-on exposure to MLOps tooling (MLflow, Kubeflow, Airflow, model serving frameworks)
Infrastructure-as-code experience (Terraform, Ansible, Pulumi)
Kubernetes or other container orchestration experience
Monitoring and observability stack experience (Prometheus, Grafana, Loki, ELK)
GPU workload or ML infrastructure exposure
Relevant certifications (cloud provider associate-level, networking, or security)