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Senior Staff Software Engineer - AI Governance

Atlanta, Georgia | Engineering

The Challenge

 
 

As a Senior Staff Software Engineer, you will serve as a technical leader for OneTrust’s AI Governance (AIG) platform, driving the design, scalability, and reliability of systems that enable enterprises to deploy and govern AI and LLM-powered applications responsibly. You will deeply understand how customers build, deploy, and operate AI systems, and translate those needs into secure, compliant, and observable platform capabilities.  

Your Mission

  
 Development    
  • Lead the design and development of Java/Python microservices and shared libraries integrating with AI platforms for OneTrust’s AI Governance product.
  • Design, build, and test cloud-native applications deployed on Microsoft Azure using Core Java, REST, and the Spring ecosystem.
  • Lead the architecture and development of reusable AIG reporting and dashboard capabilities that integrate 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 well-defined 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. 
Collaboration     
  • Work closely with UX, Product Managers and/or Product Owners, as well as other developers to contribute to planning and grooming sessions and drive team 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.
  • Contribute to sprint planning and delivery.  
Support     
  • 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.
  • Improve system monitoring, resilience, and operational maturity.  
Code Review   
  • Set standards for clean, secure, and maintainable code.
  • Conduct thorough reviews for distributed and AI-enabled systems.
  • Guide engineers on testing, automation, and AI system validation. 
Lead   
  • Help build high-performing teams by mentoring the team on new technical skills. 
  • Influence technical strategy and long-term platform direction.
  • Build a culture of ownership, learning, and engineering excellence.  
  • Lead multi-quarter technical roadmaps across teams and make practical build-versus-reuse decisions. 

You Are

  
 A senior technical leader with strong system design and hands-on execution skills, who understands how enterprises use AI and LLMs in real-world workflows and can translate those needs into scalable, secure, and compliant platform solutions.  

Your Experience Includes

   
 
  • 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. 

Extra Awesome

   
  • 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 is highly desirable.
  • Background in enterprise, regulated, or security-focused environments. 

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