To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Software Engineering
Job Details
The Experience:
Commerce Cloud Engineering is transforming how we build and ship software by putting AI at the center of the developer workflow. We're hiring a Lead Software/Release Engineer to build the tooling, automation, and release infrastructure that make AI a first-class part of how our engineers code, review, test, and deploy.
This is a hands-on lead role. You'll write production code, set technical direction, and own the systems that move AI-native development from experiment to everyday practice across a large engineering org. Your impact is measured by how much faster, safer, and smarter the engineers around you ship.
What You'll Actually Be Doing:
- Build and operate tooling that integrates AI/LLM capabilities into the software delivery lifecycle - code generation, automated review, test authoring, and release automation.
- Design and automate CI/CD systems that incorporate AI-driven quality gates, anomaly detection, and release-risk analysis.
- Own release pipelines for a large-scale enterprise platform: build, test orchestration, artifact management, deployment, and rollback.
- Define the processes and guardrails for safe, effective AI adoption across engineering teams - from prompt and tooling standards to evaluation and rollout.
- Instrument the developer workflow and measure AI-adoption impact with data: cycle time, quality, throughput, and developer experience.
- Write and review production-grade code (Java, plus pipeline/scripting languages) that other engineers depend on daily.
- Set technical direction and standards; mentor engineers and lead through influence.
- Partner with platform, QE, and product teams to remove friction and accelerate AI-native delivery.
You're Our Person If You Have:
- 8+ years in software engineering, with deep experience in release engineering, build systems, CI/CD, or developer tooling at scale.
- Strong coding ability in Java (or a comparable JVM/strongly-typed language) and experience with build tooling such as Gradle/Maven/Bazel.
- Hands-on expertise with CI/CD platforms (Jenkins, GitLab CI, GitHub Actions, or similar) and pipeline-as-code.
- Practical experience applying AI/LLM tooling to engineering workflows - coding assistants, automated review, agentic tooling, or LLM-backed automation.
- A track record of leading technical initiatives and mentoring engineers without formal authority.
- Experience defining process and measuring outcomes: you turn adoption goals into instrumented, data-driven results.
- A bias toward automation, reliability, and measurable impact.
- Building or evaluating LLM-based developer tools (prompt engineering, eval harnesses, agent frameworks, MCP).
- Cloud infrastructure and containerization (AWS/GCP, Docker, Kubernetes).
- Observability and metrics tooling for build/deploy health and developer productivity (e.g., Splunk, Grafana, ELK stack).
- Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
- Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
- Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
- Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance .
- A related technical degree required.
Even Better If You Have:
- Experience with Salesforce or Commerce Cloud (B2C/ECOM) platforms.
- Experience with other cloud providers (like GCP).
- Contributions to open-source projects.
- Certifications in AWS or related DevOps technologies.
Why This Role?
You'll be at the leading edge of how a major engineering org adopts AI - not as a side project, but as core infrastructure. We value pragmatic automation, data-driven decisions, and a high bar for reliability. If you want leadership and hands-on engineering without the tradeoff, and you want to shape how AI changes the way software gets built, this is it.
