Senior Staff Software Engineer - AI Governance

OneTrust•Atlanta, GA

2d•$165,000 - $247,500•Hybrid

About The Position

OneTrust is hiring a Senior Staff Software Engineer to provide technical leadership for its AI Governance (AIG) platform. The role drives the design, scalability, and reliability of systems that help enterprises responsibly deploy and govern AI- and LLM-powered applications. The engineer will understand customer AI workflows and translate governance needs into secure, compliant, observable platform capabilities. The position leads development of Java and Python microservices, shared libraries, cloud-native Azure applications, reusable reporting and dashboard capabilities, semantic and metric abstraction systems, and event-driven data pipelines. It works with governance data, analytical platforms, and AI observability signals, and partners with frontend engineering to deliver dashboard and drill-down experiences. The role also owns technical design for critical systems and strengthens automated testing, monitoring, resilience, and operational maturity. As a senior technical leader, this engineer shapes platform architecture and multi-quarter roadmaps, aligns teams on design and priorities, mentors engineers, and promotes secure, maintainable engineering practices. The role collaborates with Product, UX, Customer Success, security, platform, observability, and engineering teams to deliver capabilities that meet customer AI governance needs. OneTrust describes its culture as office-first, encouraging three days per week in the office for most roles, while noting that requirements or flexibility may vary by role. The position may be eligible for discretionary bonuses, equity, and/or commissions, in addition to benefits. For candidates based in specified states, the listed annual base pay range is $165,000–$247,500.

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 AIG reporting and dashboard capabilities that combine governance data from SQL databases and analytical platforms 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 consume normalized observability events and produce dashboard-ready datasets and derived metrics.
Integrate with the AI Observability platform through APIs and data contracts for 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 and/or Product 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; drive alignment across teams on platform design and priorities; and contribute to sprint planning and delivery.
Reduce defects by identifying and addressing issues early in the development lifecycle.
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 on new technical skills, 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.