Sr Cyber Security Automation Engineer
New
J
JLLCommercial Real Estate
United StatesFull-TimeSenior
Salary150,000 - 190,000 USD per year
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Job Details
- Experience
- 5+ years Information Technology experience, 4+ years of Cyber Security Experience, 2+ years of any SOAR tool experience, 4+ years of experience developing with Python
- Required Skills
- PythonBashCybersecurityCI/CDRESTful APIsTerraformGitHubLLM
Requirements
- Master’s or Bachelor’s degree in Information & Computer Science, Statistics, or a quantitative science discipline.
- 5+ years of experience in Information Technology.
- 4+ years of experience in Cyber Security.
- 4+ years of experience developing with Python.
- 2+ years of experience with SOAR tools (e.g., Splunk SOAR, Sentinel, Swimlane).
- Working knowledge of scripting languages including Bash and PowerShell.
- Demonstrated experience building solutions using OpenAI or other LLMs.
- Experience working with REST and other third-party API integrations.
- Strong understanding of IT security concepts and practices with experience automating workflows.
- Proficiency in GitHub for version control and CI/CD tools (e.g., GitHub Actions) for automation.
- Familiarity with infrastructure-as-code (IaC) tools such as Terraform or CloudFormation.
- Strong verbal and written communication skills with ability to manage time effectively in an agile environment.
Responsibilities
- Work with SOC, Insider Risk, DLP, Red Team, Threat Intel, and other security functions to design, deliver and maintain efficient, reusable, and reliable security automations.
- Manage the whole lifecycle of an automation playbook from requirements gathering and planning to design, testing, implementation, and maintenance.
- Lead initiatives to identify security automation opportunities, obtain stakeholder buy-in, deploy solutions, and measure efficiency gains.
- Leverage Large Language Models (OpenAI, Hugging Face, etc.) to automate actions and multiply security analysts' capabilities.
- Collaborate with the detection team to build end-to-end solutions with automated feedback loops to improve data enrichment and detection tuning.
- Provide quality checks on automations to reduce errors and enforce continuous improvement.
- Advocate for standardization across team automation capabilities.
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