Siemens Experience and Platform Engineering organization is seeking a highly proactive and results-driven Release Automation Engineer to join our dynamic team. The ideal candidate will be responsible for ensuring end-to-end accountability for releases, actively enforcing release criteria, and implementing robust processes and guardrails to enhance quality and reliability.

As a Release Automation Engineer, you will release product software after completing the testing and deployment stages and work closely with the application development team, testing team, and production team. You will maintain proper coordination between these teams to update project-related information.

This position drives overall organizational agile transformation and release train engineering activities as part of the Developer Experience and Process organization. This includes but is not limited to the function of release management ensuring all conditions are met through automated processes in accordance with change management, test, and continuous deployment policies prior to deployment to the production environment.

In addition, this role will leverage Agentic AI capabilities to optimize release workflows, automate decision-making, and proactively identify risks and dependencies across the release pipeline. The successful candidate will integrate AI-driven agents to enhance predictive analytics, improve deployment reliability, and streamline communication between cross-functional teams. Agentic AI will be utilized to enforce compliance, monitor system health, and dynamically adjust release strategies based on real-time insights, ensuring faster, smarter, and more resilient delivery cycles.

This role requires strong leadership, firm decision-making, and a hands-on approach to drive efficiency and automation in deployments while maintaining the highest standards of quality and stability. The Release Automation Engineer will collaborate closely with cross-functional teams, including development, QA, security, and operations, to ensure seamless software releases and continuous improvement in automated release management and client notification practices. Seeking a confident individual looking to work in an exciting, fast-paced environment, steering the organization with a proactive and hands-on approach.

Key Responsibilities

• Own end-to-end release processes, ensuring compliance with organizational standards.
• Drive automation and efficiency in CI/CD pipelines, Jira process and deployment workflows.
• Implement Agentic AI-driven solutions for predictive analytics, risk detection, and intelligent release governance.
• Collaborate with development, QA, security, and operations teams to ensure seamless releases.
• Monitor system health and dynamically adjust release strategies using AI insights.
• Support organizational agile transformation and automated release engineering

Required Skills and Experience

• Cloud DevOps Expertise: Strong experience with CI/CD pipelines, release automation, and cloud platforms (AWS, Azure).
• Agentic AI Integration:
• Hands-on experience with AI-driven automation tools and frameworks.
• Knowledge of Agentic AI principles—autonomous agents, goal-oriented workflows, and dynamic decision-making.
• Ability to design and implement AI agents for predictive analytics, risk detection, and automated release governance.
• Familiarity with AI orchestration platforms and integration into DevOps pipelines.
• Programming & Scripting: Proficiency in languages such as Python, Bash, or PowerShell for automation.
• Monitoring & Observability: Experience with tools like Prometheus, Grafana, Datadog for system health and release tracking.
• Agile & Release Management: Strong understanding of Agile methodologies, release train engineering, and change management.
• Security & Compliance: Knowledge of secure deployment practices and compliance enforcement.
• Collaboration & Leadership: Excellent communication skills and ability to lead cross-functional teams.

Preferred Qualifications

• Advanced experience with AI/ML models for automation and predictive analytics.
• Familiarity with reinforcement learning for autonomous agent optimization.
• Experience integrating Large Language Models (LLMs) into DevOps workflows for intelligent decision-making and automated documentation.
• Knowledge of AI governance and ethical frameworks for responsible deployment.
• Exposure to cloud-native AI services
• Strong understanding of data engineering principles for feeding AI agents with real-time operational data.