Lead Engineer - AI Trust & Governance

About the role

Agentforce is the future of AI, and you are the future of Salesforce. We are seeking a highly skilled, hands-on, and deeply technical Software Development Engineer to help build our AI Governance platform from the ground up.

Responsibilities

• 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
• AI-Assisted Engineering: Use AI development tools such as Claude and other coding assistants as part of the software development lifecycle to accelerate delivery, improve code quality, prototype faster, and enhance engineering productivity
• 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
• 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
• CI/CD Delivery Process Knowledge: Bring practical knowledge of CI/CD concepts, automated testing, and deployment workflows, and release management practices to help ensure the platform can be delivered reliably across environments
• Architecture and Technical Design: Define and drive the overall platform architecture, including service design, API strategy, data flows, integration patterns, event-driven workflows, and system scalability considerations
• 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
• Observability and Telemetry: Build observability components that capture logs, metrics, traces, and runtime telemetry across platform services, enabling deeper diagnostics, issue detection, root cause analysis, and ongoing operational intelligence
• 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
• Technical Leadership and Ownership: Provide strong technical leadership across the stack, establish engineering standards, influence design decisions, mentor other engineers, and take ownership of delivering a strategic platform from the ground up
• Cross Functional Collaboration: Partner closely with product, architecture, security, compliance, governance, and engineering stakeholders to translate business goals and trust requirements into scalable technical solutions

Requirements

• 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
• 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

Preferred Qualifications

• Experience with Salesforce Ecosystem
• Experience building or supporting AI governance, model governance, risk, trust, compliance, or observability platforms
• Experience with Gen AI applications, LLM-powered systems, agentic workflows, and model evaluation frameworks
• Experience with MLOps, LLMOps, or AI platform engineering, including model lifecycle tolling and development controls
• Familiarity with data privacy, model risk, or regulatory considerations in enterprise AI environments
• Experience in regulated or trust-sensitive industries where system reliability, governance, and control are critical
• Experience designing systems for auditability, lineage, traceability, and evidence management