Senior Staff Software Engineer - AI Governance

OneTrust•San Francisco, CA

2d•$193,050 - $257,400•Hybrid

About The Position

As a Senior Staff Software Engineer, you will serve as a technical leader for OneTrust’s AI Governance platform, driving the design, scalability, and reliability of systems that enable enterprises to deploy and govern AI- and LLM-powered applications responsibly. You will understand how customers build, deploy, and operate AI systems and translate those needs into secure, compliant, and observable platform capabilities. The role leads development of Java and Python microservices, shared libraries, reusable reporting and dashboard capabilities, semantic and metric abstractions, and event-driven integrations. Systems integrate governance data with runtime observability signals and are built for cloud-native deployment, scalability, security, and reliability. You will collaborate across engineering, product, UX, customer success, security, platform, and observability teams, while guiding technical strategy, roadmaps, code quality, testing, and team development.

Requirements

Bachelor’s or master’s degree in computer science, engineering, or a related technical or business field.
12+ years of professional software engineering and development experience.
Strong expertise in Java/J2EE, Spring, Python, design patterns, microservices, and cloud-native distributed systems.
Proven experience leading reusable platform, reporting, analytics, or data products that support multiple teams or dashboards.
Hands-on experience with Cube.js, semantic layers, metric layers, or comparable metric/query abstraction systems.
Hands-on experience with Apache Flink for real-time stream processing, windowed aggregation, event correlation, or derived-metric generation.
Experience designing event-driven services and data-ingestion pipelines using Kafka, including integrations with SQL databases, analytical platforms, APIs, and observability systems.
Strong experience with data modeling, dataset contracts, pre-aggregation, read models, caching, query optimization, and data-quality controls.
Experience building secure, multi-tenant SaaS platforms with tenant isolation, authentication, authorization, and secure service-to-service communication.
Experience working with AI/LLM platforms and production AI workloads, including model orchestration, RAG, evaluation, guardrails, or runtime observability.
Good understanding of REST, OAuth, JSON, SQL, NoSQL databases, and Python or Bash scripting.
Experience with CI/CD, automated testing, production monitoring, incident response, and Agile development practices.
Experience with Azure, AWS, Google Cloud, and open-source technologies.
Ability to lead multi-quarter roadmaps, make practical build-versus-reuse decisions, and align engineering, product, UX, security, platform, and observability teams.

Nice To Haves

Experience with OpenTelemetry, VictoriaMetrics, Prometheus/MetricsQL, Grafana Loki/LogQL, Grafana Tempo/TraceQL, Alertmanager, or Open Policy Agent.
Experience integrating with Databricks, Delta Lake, Power BI, or other analytical data platforms.
Experience building configurable dashboard systems, data visualization frameworks, or design-system-based frontend platforms.
Experience with AI governance, model risk management, compliance, GRC, audit evidence, or regulatory reporting.
Experience with graph data models, dependency maps, entity resolution, or supply-chain analysis.
Experience designing SDK telemetry, model gateways, runtime instrumentation, or post-market monitoring.
Proficiency in Kubernetes, including cluster deployment, scaling, and management.
Background in enterprise, regulated, or security-focused environments.

Responsibilities

Lead the design and development of Java/Python microservices and shared libraries integrating with AI platforms for the AI Governance product.
Design, build, and test cloud-native applications deployed on Microsoft Azure using Core Java, REST, and the Spring ecosystem.
Lead architecture and development of reusable governance reporting and dashboard capabilities that combine SQL and analytical-platform data with runtime observability signals.
Design reusable semantic-layer and metric-abstraction capabilities, including dataset contracts, metric definitions, query APIs, pre-aggregation, caching, filtering, time-period selection, and drill-down workflows.
Lead event-driven integrations using Kafka and Apache Flink to process normalized observability events and produce dashboard-ready datasets and derived metrics.
Integrate with the AI Observability platform through APIs and data contracts covering runtime metrics, logs, traces, alerts, policy decisions, and evidence.
Own technical design for critical systems, ensuring scalability, security, and reliability.
Partner with frontend engineers to build reusable dashboard shells, visualization components, filters, data tables, and entity drill-down experiences.
Maintain strong automated unit and integration test coverage in line with team standards.
Work with UX, product managers or owners, and developers in planning and grooming, and lead discussions on system architecture and component design.
Partner with Product, UX, and Customer Success to understand customer AI use cases and governance needs.
Lead architecture discussions and technical planning, and drive alignment across teams on platform design and priorities.
Contribute to sprint planning and delivery.
Identify and address issues early, reduce defects, and ensure critical and high-priority CII’s are delivered per SLA.
Promote early defect detection through strong design and testing, and improve system monitoring, resilience, and operational maturity.
Set standards for clean, secure, and maintainable code; conduct thorough reviews for distributed and AI-enabled systems; and guide engineers on testing, automation, and AI system validation.
Mentor team members, influence technical strategy and long-term platform direction, and foster ownership, learning, and engineering excellence.
Lead multi-quarter technical roadmaps across teams and make practical build-versus-reuse decisions.