Lead Engineer - AI Trust & Governance

Description We are seeking a highly skilled, hands‑on, and deeply technical Software Development Engineers to help build our AI Governance platform from the ground up. This is a critical senior role responsible for designing and developing both the front‑end and back‑end foundations of a platform that enables safe, trusted, and scalable AI deployment across the enterprise. This role will directly support our number one value: Trust. You will help architect and deliver a platform that spans governance, intake workflows, lifecycle management, monitoring, observability, risk controls, and operational tooling for AI systems and agents. We are looking for a builder who is comfortable wearing multiple hats across software engineering, cloud infrastructure, platform engineering, developer tooling, and user experience. You should be energized by ambiguity, excited to build systems from scratch, and motivated by solving difficult problems at the intersection of AI, governance, trust, observability, and enterprise scale. What You’ll Do 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. 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. What We’re Looking For 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. Preferred Qualifications (Bonus Points) 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. For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records. #J-18808-Ljbffr B Capital