About the Role 

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)