We are looking for a DevSecOps & Engineering Enablement Engineer to establish and operate a consistent, secure, and reliable process for moving code from development to production for the future SSOE platform.
You will centrally manage the tools, pipelines, standards, and automation that engineering teams use to build, test, review, secure, and deploy their code.
The objective is to ensure that code progressing towards production is not only deployed successfully, but is also functionally working, secure, tested, and of the required quality. Relevant security, testing, and quality gates should be built directly into the delivery process and applied consistently across engineering teams.
You will also drive the responsible use of AI within the software development lifecycle, including AI-assisted code review, testing, security analysis, documentation, and engineering feedback.
What You Will Be Working On
As a DevSecOps & Engineering Enablement Engineer, you will provide the common engineering capabilities used by application, observability, platform, data, and infrastructure teams across SSOE Common Services.
Engineering teams remain responsible for the software and services they build. You will own and continuously improve the paved road from code to production.
The aim is to provide engineering teams with a common and automated route from development to production, with security, quality, and functional verification built into the process rather than performed separately at the end.
Key Responsibilities
Software Delivery and CI/CD
- Own and operate the common CI/CD capabilities used across SSOE Common Services
- Design and maintain reusable pipelines for building, testing, validating, and deploying software
- Establish consistent processes for promoting changes through development, testing, staging, and production environments
- Define the security, quality, testing, and approval gates required at each stage of the software delivery lifecycle
- Automate deployment and post-deployment functional verification wherever appropriate
- Ensure applications and services are verified to be healthy and functioning as expected before a deployment is considered successful
- Support deployments across on-premise, GCC, AWS, Azure, and hybrid environments
- Ensure failed deployments can be stopped, retried, recovered, or rolled back safely
- Reduce manual deployment activities through repeatable and auditable automation
- Continuously improve pipeline reliability, execution time, developer feedback, and deployment success rates
Security and Quality Gates
- Build appropriate security and quality checks directly into CI/CD pipelines
- Automatically check source code, dependencies, containers, configuration, and Infrastructure as Code for relevant security issues
- Detect credentials, secrets, or sensitive information accidentally introduced into source repositories
- Integrate automated unit, integration, and other relevant tests into the delivery process
- Establish code-quality checks and minimum standards before changes can progress
- Define appropriate approval requirements for higher-risk or production changes
- Ensure failed mandatory checks prevent changes from progressing until the issue is addressed
- Work with engineering and security teams to determine which controls should be mandatory and which should provide advisory feedback
- Regularly review gates to ensure they continue to provide useful assurance without introducing unnecessary delivery delays
Functional Verification
- Define standard approaches for verifying that applications and services are functioning correctly after deployment
- Automate smoke tests, health checks, integration tests or other appropriate post-deployment validation
- Ensure deployment success is based on the actual health and functionality of the deployed service, not simply whether the deployment command completed successfully
- Integrate observability and application health information into deployment verification where appropriateAutomatically stop or roll back deployments when critical validation fails
- Work with application teams to establish meaningful acceptance criteria for production deployments
AI-Assisted Software Development
- Introduce appropriate AI capabilities into code review and software delivery workflows
- Use AI to assist engineers in identifying possible defects, security issues, maintainability concerns, and engineering-standard violations
- Explore AI-assisted generation and improvement of automated tests
- Use AI to assist with technical documentation and explanations of code changes
- Explore AI-assisted diagnosis of failed builds, tests, security checks, and deployments
- Establish clear rules governing what source code, configuration, and information may be provided to AI services
- Ensure engineers remain responsible for reviewing and approving AI-generated code and recommendations
- Measure whether AI