DevOps & Engineering Enablement Engineer

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

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