Lead Release/software Engineering

Salesforce Salesforce · Enterprise · Burlington, MA

Lead Release/Software Engineer at Salesforce focused on building and operating tooling, automation, and release infrastructure that integrates AI/LLM capabilities into the software delivery lifecycle. This includes CI/CD systems with AI-driven quality gates, code generation, automated review, test authoring, and release automation for a large-scale enterprise platform. The role involves setting technical direction, mentoring engineers, and measuring the impact of AI adoption on developer workflows.

What you'd actually do

  1. Build and operate tooling that integrates AI/LLM capabilities into the software delivery lifecycle - code generation, automated review, test authoring, and release automation.
  2. Design and automate CI/CD systems that incorporate AI-driven quality gates, anomaly detection, and release-risk analysis.
  3. Own release pipelines for a large-scale enterprise platform: build, test orchestration, artifact management, deployment, and rollback.
  4. Define the processes and guardrails for safe, effective AI adoption across engineering teams - from prompt and tooling standards to evaluation and rollout.
  5. Instrument the developer workflow and measure AI-adoption impact with data: cycle time, quality, throughput, and developer experience.

Skills

Required

  • 8+ years in software engineering
  • 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)
  • 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
  • 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
  • Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows
  • Contribute to building and maintaining the shared system context
  • Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance
  • A related technical degree required

Nice to 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

What the JD emphasized

  • AI/LLM capabilities
  • AI-driven quality gates
  • AI adoption
  • AI agents
  • AI-native development
  • AI development tools
  • AI-generated code
  • AI development tools
  • AI as a core part of your development workflow
  • AI agents integrate seamlessly
  • AI tools
  • AI
  • AI

Other signals

  • AI-native development
  • AI-driven quality gates
  • AI adoption across engineering teams
  • AI agents integrate seamlessly into human workflows