DevOps / Software Automation Engineer

CYNET SYSTEMS INC.
Austin, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Austin, United States of America

Tech stack

API
Artificial Intelligence
Audit Trail
System Configuration
Continuous Integration
Data Visualization
Software Debugging
Linux
DevOps
Github
Monitoring of Systems
Python
Linux System Administration
Performance Tuning
Power BI
Ansible
Prometheus
Shell Script
Workflow Management Systems
Zabbix
Data Processing
Performance Testing
Grafana
Spark
Kubernetes
Infrastructure Automation Frameworks
Information Technology
Data Analytics
Kafka
Data Management
Machine Learning Operations
Docker
Server Operating Systems & Platforms
Jenkins
Databricks
Data Generation

Job description

  • Responsible for designing and implementing automated infrastructure and performance benchmarking workflows.
  • The role focuses on building CI/CD pipelines, automating CPU performance testing processes, enabling scalable execution systems, and supporting data-driven performance engineering environments with high reliability and repeatability., * Design and implement automated workflows for CPU performance benchmarking including setup, execution, validation, and reporting.
  • Translate manual performance engineering processes into scalable automation pipelines.
  • Enable CI-triggered or one-click benchmark execution with standardized results.
  • Automate log parsing, metrics extraction, and structured data generation.
  • Build and maintain CI/CD pipelines for benchmark execution and infrastructure workflows.
  • Ensure reproducibility, traceability, and auditability of performance runs.
  • Automate server provisioning, OS deployment, and system configuration.
  • Manage Linux-based environments optimized for performance testing.
  • Monitor platform health and benchmark execution using observability tools.
  • Debug and resolve failures during automated runs and support performance engineers.
  • Perform capacity planning and scale infrastructure for increasing workloads.
  • Process and structure benchmark data using Python and data platforms.
  • Support dashboards and reporting for engineering stakeholders.
  • Document workflows, architectures, and troubleshooting procedures.
  • Collaborate closely with performance engineers and internal IT teams.
  • Support networking, hardware, and security alignment where needed.

Requirements

  • Bachelor s degree in Computer Science, Engineering, or equivalent practical experience.
  • Strong proficiency in Python and Linux shell scripting.
  • Hands-on experience with CI/CD tools such as Jenkins and GitHub.
  • Strong understanding of Linux systems, OS tuning, and server environments.
  • Experience automating infrastructure using Ansible or similar tools.
  • Ability to debug complex system and automation issues independently.
  • Strong communication skills and ability to collaborate with engineering teams.
  • Experience working in performance-focused or automation-heavy environments.

Experience:

  • Experience building and maintaining CI/CD pipelines for automation workflows.
  • Experience in infrastructure provisioning and system configuration at scale.
  • Experience with containerization using Docker and orchestration tools such as Kubernetes.
  • Experience with monitoring and observability tools.
  • Experience in performance benchmarking or system performance environments preferred.
  • Experience processing and analyzing large-scale system or log data., * Experience with CPU or system performance benchmarking tools.
  • Familiarity with Spark, Kafka, or Databricks.
  • Experience with Prometheus, Grafana, Zabbix, or similar monitoring tools.
  • Experience with Power BI or other visualization tools.
  • Strong systems thinking and architecture design skills.
  • Experience building backend services in Go or similar languages.
  • Exposure to MCP development and service integration patterns.
  • Exposure to MLOps and AI/ML pipeline workflows.

Skills:

  • Python and Linux shell scripting.
  • Jenkins, GitHub, CI/CD pipelines.
  • Linux system administration and tuning.
  • Ansible and infrastructure automation.
  • Docker and Kubernetes.
  • Performance benchmarking and automation.
  • Observability and monitoring tools.
  • Data processing and analytics.
  • System debugging and troubleshooting.
  • API and service integration.

Qualification And Education:

  • Bachelor s degree in Computer Science, Engineering, or related field required.

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