Cyber Security Engineer Automation

Job Description

The Junior Automation Security Engineer builds and supports practical automations that make security controls easier to operate, validate, and scale. This is a hands‑on engineering role for a curious builder who can write clean Python, work confidently in Git Hub and Git Hub Actions, and learn how Terraform and AWS services fit together. The engineer will use approved internal AI tools to accelerate research, prototyping, and documentation, while developing simple, secure agents and workflow automations under senior guidance.

• Build and maintain Python scripts, CLIs, API integrations, and scheduled jobs that reduce repetitive security and operational work.
• Automate evidence collection, configuration checks, alert enrichment, ticket creation, reporting, and basic remediation workflows.
• Write unit tests, logging, error handling, and concise documentation so automations are safe to operate and easy to support.

• Contribute to repositories using pull requests, code review, branching, issue tracking, and dependency hygiene.
• Create and maintain Git Hub Actions workflows for linting, testing, scanning, packaging, and controlled deployment tasks.
• Read, update, and test Terraform modules and plans with guidance; help identify insecure cloud configuration patterns before release.

• Work with core AWS concepts including IAM, VPC, Cloud Trail, Cloud Watch, S3, Lambda, EC2, Secrets Manager, and AWS Organizations.
• Help automate cloud inventory, access reviews, configuration validation, tagging checks, and security findings triage.
• Follow least‑privilege, secrets‑management, logging, and change‑control practices in all automation work.

• Use approved internal AI coding and productivity tools responsibly for code exploration, test generation, documentation, and prototyping.
• Practice AI‑assisted or “vibe coding” in a disciplined way: validate generated code, protect confidential data, and submit reviewed changes.
• Help develop narrowly scoped internal agents that call approved tools, retrieve permitted knowledge, and keep a human approval step for impactful actions.
• Learn core AI security risks, including prompt injection, over‑permissioned tools, data leakage, and unsafe agent behavior.

• Partner with security, platform, cloud, SOC, and engineering teams to turn recurring problems into reliable automations.
• Participate in incident follow‑up, backlog refinement, peer review, and knowledge sharing.

• Working proficiency in Python and REST APIs; able to read, debug, and improve scripts written by others.
• Hands‑on experience with Git and Git Hub; familiarity with CI/CD concepts and Git Hub Actions.
• Fundamental understanding of AWS services, IAM, networking, logging, and infrastructure‑as‑code concepts.
• Basic experience with Terraform or a strong willingness to learn it through real repository work.
• Comfort learning unfamiliar tools, asking sound technical questions, and documenting work clearly.
• Security mindset: understands the importance of least privilege, secrets handling, code review, and change control.

• Security tooling, SIEM/SOAR, vulnerability management, ticketing, or cloud operations experience.
• Exposure to container technologies, Docker, Kubernetes, or policy‑as‑code.
• Experience using enterprise AI tools, LLM APIs, RAG, MCP, or agent frameworks in a controlled environment.
• AWS Cloud Practitioner, AWS Solutions Architect Associate, Security+, or comparable learning path.

• Useful, tested automations reduce manual effort and are adopted by their intended teams.
• Git Hub Actions and Terraform changes are reviewed, traceable, and meet agreed security checks.
• Cloud configuration findings are surfaced accurately and remediated efficiently.
• AI‑enabled workflows stay within approved data, access, and human‑approval boundaries.

• Delivers scoped work with guidance and grows into independent ownership of well‑defined automations.
• Escalates ambiguity or risk early and uses…