The Senior Automation Security Engineer designs and delivers scalable automation that strengthens the organization’s security posture and engineering velocity. This role combines strong Python engineering with deep working knowledge of GitHub/GitHub Actions, Terraform, and AWS. The engineer leads secure automation patterns, enables disciplined internal use of AI tools, and builds production-quality agentic workflows with appropriate governance, observability, and human control.

What You Will Do

Automation Platform Engineering

• Design, build, and operate reusable Python services, libraries, CLIs, and integrations that automate security controls and operational workflows.
• Establish engineering standards for reliability, testing, observability, secrets handling, access control, and lifecycle ownership of automations.
• Automate high-value workflows such as identity and access reviews, cloud configuration remediation, evidence collection, security findings enrichment, exception management, and incident response orchestration.

GitHub, GitHub Actions & DevSecOps

• Own and improve GitHub Actions patterns for secure CI/CD, including reusable workflows, OIDC-based cloud access, artifact controls, approvals, and policy checks.
• Integrate code scanning, dependency controls, secrets detection, IaC scanning, tests, and release gates into engineering workflows without unnecessary delivery friction.
• Review workflow and repository permissions, third-party actions, supply-chain risks, and runner security; drive pragmatic remediation.

Terraform & AWS Security Automation

• Design and maintain Terraform modules, guardrails, and policy-as-code controls for AWS environments.
• Automate secure AWS configuration across IAM, Organizations, VPC, CloudTrail, CloudWatch, S3, KMS, Lambda, ECS/EKS, Secrets Manager, and related services.
• Build safe detection and remediation flows using least privilege, change controls, dry runs, rollback approaches, and audit-ready logging.

Internal AI Enablement & Agent Development

• Develop governed internal AI automations and agents that use approved models, tools, knowledge sources, and identity boundaries.
• Design agent workflows with explicit tool scopes, input validation, prompt-injection defenses, audit logs, evaluation cases, and human-in-the-loop approvals for material actions.
• Create reusable patterns for tool calling, retrieval/RAG, workflow orchestration, context handling, and secure deployment; mentor teams on their use.
• Champion responsible AI-assisted or “vibe coding” practices: clear review accountability, source validation, secure code patterns, and protection of internal data.

Technical Leadership

• Translate security and operational needs into an automation roadmap, architecture, and measurable outcomes.
• Lead technical design reviews, mentor junior engineers, and partner with platform, cloud, SOC, and application teams.
• Communicate tradeoffs and risk clearly to technical and non-technical stakeholders.