Lead Engineer (AI Trust & Governance)

TL;DR

Lead Engineer (Fullstack/AI): Designing and building an AI Governance platform from the ground up with an accent on safe, trusted, and scalable AI deployment across the enterprise. Focus on architecting end-to-end governance workflows, implementing AWS cloud infrastructure, and developing observability and telemetry components.

Location: San Francisco, Seattle, Chicago, Palo Alto, New York, or Bellevue

Company

A global leader in CRM and cloud software focused on enterprise AI and trust.

What you will do

• Lead the end-to-end design, development, and scaling of the AI governance platform across both front-end and back-end components.
• Design and build secure, scalable, and resilient cloud-native infrastructure on AWS to support platform services and governance workflows.
• Develop platform capabilities to monitor, track, and manage AI and machine learning systems and agents throughout their lifecycle.
• Build observability components to capture logs, metrics, traces, and runtime telemetry for deeper diagnostics and operational intelligence.
• Design and develop Generative AI capabilities, including LLM-powered features, intelligent workflows, and agent-based functionality.
• Establish engineering standards and provide technical leadership through mentoring and ownership of a strategic platform.

Requirements

• 10+ years of professional software development experience with depth in full-stack development.
• Strong hands-on expertise in API design, distributed systems, and modern front-end frameworks.
• Proven experience building complex platforms or enterprise applications from scratch.
• Deep experience with AWS and cloud-native architecture for production-grade systems.
• Demonstrated ability to build and scale CI/CD pipelines and automated deployment workflows.
• Experience implementing monitoring, logging, and telemetry for operational insight.

Nice to have

• Experience within the hirify.global ecosystem.
• Background in building AI governance, risk, trust, or compliance platforms.
• Experience with MLOps, LLMOps, and model evaluation frameworks.
• Familiarity with data privacy, model risk, and regulatory considerations in enterprise AI environments.
• Experience designing systems for auditability, lineage, and traceability.