Lead Engineer - AI Trust & Governance

Salesforce Salesforce · Enterprise · San Francisco, California - Palo Alto, Illinois - Chicago, New York - New York, Washington - Seattle, Washington - Bellevue, CA

Salesforce is seeking a Lead Engineer to build an AI Governance platform from the ground up. This role involves full-stack development, AWS cloud infrastructure, ML/AI platform services, CI/CD, and defining platform architecture. The engineer will focus on monitoring, observability, and building generative AI capabilities, including LLM features and agent-based functionality, ensuring safe, trusted, and scalable AI deployment across the enterprise. Experience with AI governance, risk, trust, and compliance is preferred.

What you'd actually do

  1. Full Stack Platform Development: Lead the end-to-end design, development, and scaling of the AI governance platform, building both the front-end and back-end components that support enterprise wide AI governance
  2. AWS Cloud Infrastructure Development: Design and build secure, scalable, and resilient cloud native infrastructure on AWS to support platform services, governance workflows, system integrations, and application performance at enterprise scale
  3. ML and AI Platform Services: Build and support platform capabilities that enable AI and machine learning systems to be governed, monitored, tracked, and managed throughout their lifecycle, including services that support model and agent operations
  4. Monitoring and Operational Visibility: Develop monitoring capabilities that provide insight into system health, application performance, workflow execution, service reliability, and platform usage across the governance ecosystem
  5. Generative AI Platform Development: Assist with designing and developing Generative AI capabilities as part of the platform, including LLM powered features, intelligent workflows, agent-based functionality, and other AI native applications

Skills

Required

  • 10+ years of professional software development experience with significant depth across both front-end and back-end development
  • Strong hands-on expertise in full stack development, including modern front-end frameworks, API design, distributed systems, and back-end application development.
  • Proven experience building complex platforms or enterprise applications from scratch
  • Deep experience with AWS and cloud-native architecture, including designing scalable, secure, and production grade systems.
  • Strong experience with platform engineering, developer infrastructure, and production software delivery practices
  • Demonstrated ability to build and scale CI/CD pipelines, automated frameworks, and deployment workflows
  • Experience building systems with strong monitoring, observability, logging, telemetry, and operational insight capabilities
  • Strong architectural judgment
  • Experience working in environments where security, compliance, governance, and auditability are important design considerations
  • Comfort working across ambiguity and leading technical execution in highly visible, high-impact initiatives
  • Excellent collaboration and communication skills
  • Demonstrated experience using Generative AI as part of the software development lifecycle

Nice to have

  • Experience with Salesforce Ecosystem
  • Experience building or supporting AI governance, model governance, risk, trust, compliance, or observability platforms

What the JD emphasized

  • build our AI Governance platform from the ground up
  • building systems from scratch
  • AI, governance, trust, observability, and enterprise scale
  • governance, intake workflows, lifecycle management, monitoring, observability, risk controls, and operational tooling for AI systems and agents
  • governed, monitored, tracked, and managed throughout their lifecycle
  • monitoring, observability, logging, telemetry, and operational insight capabilities
  • security, compliance, governance, and auditability are important design considerations

Other signals

  • AI Governance platform
  • trusted and scalable AI deployment
  • AI systems and agents
  • Generative AI capabilities
  • LLM powered features
  • agent-based functionality