Job Summary
As a Mid-Level DevOps Engineer, you will design, implement, and manage cloud infrastructure, deployment pipelines, and automation systems that support scalable applications and machine learning workloads. You will work independently on infrastructure components while collaborating with cross-functional teams to improve performance, reliability, and security of production environments.
Key Responsibilities
- Infrastructure Deployment & Maintenance: Design and deploy infrastructure on AWS and GCP, ensuring stability, scalability, and cost-effectiveness.
- CI/CD Pipeline Implementation: Develop and manage CI/CD pipelines for application and ML workloads, incorporating automated testing and security scanning.
- Containerization & Orchestration: Create and maintain Docker containers and manage Kubernetes clusters for scalable deployment of services.
- Monitoring & Logging: Implement and maintain observability tools (e.g., Prometheus, Grafana, Cloud Monitoring) for performance tracking and incident response.
- Infrastructure as Code (IaC): Use tools like Terraform or Cloud Deploy to automate infrastructure provisioning and updates.
- Security & Access Control: Manage IAM roles, service identities, and secure networking rules to safeguard infrastructure access.
- ML Model Deployment Support: Collaborate with ML teams to deploy models into production and implement monitoring/versioning workflows.
- Automation & Scripting: Write scripts in Python or Bash to automate operational tasks and streamline developer workflows.
- Collaboration: Work with developers, ML engineers, and DevOps peers to support and improve development and deployment pipelines.
- Documentation & Standards: Maintain documentation for infrastructure design, workflows, and access configurations. Contribute to improving DevOps practices and playbooks.
General Qualifications
- Bachelor’s degree in Computer Science, Information Technology, or a related field.
- 2–5 years of hands-on experience in DevOps, SRE, or cloud infrastructure roles.
Experience and Skills
Technical Skills:
- Strong experience with AWS and/or GCP services (IAM, EC2, S3, VPC, GKE, etc.).
- Working knowledge of CI/CD tools (e.g., GitHub Actions, Jenkins, GitLab CI) and pipeline development.
- Proficient in Docker and basic to intermediate Kubernetes usage.
- Solid experience with Terraform or equivalent IaC tools.
- Comfortable with Linux administration and basic networking.
- Experience with observability tools (Grafana, Prometheus, or similar).
- Strong scripting abilities using Bash, Python, or other automation tools.
Soft Skills:
- Strong collaboration and communication with cross-functional teams.
- Proactive mindset toward system improvement and workflow automation.
- Ability to manage time effectively and prioritize operational work.
- Critical thinker who can debug and solve infrastructure and deployment issues independently.
Preferred Experience:
- Exposure to machine learning model deployment workflows.
- Familiarity with DevSecOps practices (e.g., security scanning tools, secrets management).
- Basic knowledge of GitOps and tools like ArgoCD.
Work Environment
Office-based environment with potential for hybrid or remote work depending on company policy.
Reporting Line
This role reports directly to the Lead Software Engineer.
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