AI Trust Governance Architect

Key Responsibilities:

• Key Responsibilities
• AI Assurance Architecture
• Architect platforms and frameworks for AI assurance evaluation and benchmarking
• Design systems for LLM agent and RAG evaluation across functional non functional and risk dimensions
• Define architectural patterns for Responsible AI bias detection explainability and safety validation
• Build reusable assurance components supporting Business Assurance Risk Assurance and Reliability
• Security Reliability Governance
• Architect AI testing and validation for security privacy prompt injection and adversarial robustness
• Integrate red teaming threat simulation and chaos style validation for AI systems
• Define governance mechanisms for model usage auditability traceability and compliance
• Ensure AI systems meet enterprise standards for resilience fault tolerance and observability
• Platform Engineering Enablement
• Design AI assurance platforms supporting automated test execution reporting and insights
• Enable integration with CI CD pipelines to enforce AI quality gates
• Collaborate with QE engineering teams to embed AI assurance into the SDLC
• Mentor teams on AI risk identification and mitigation from an engineering perspective
• Core Platforms Frameworks Tooling
• LLM and AI evaluation frameworks PromptFoo DeepEval custom LLM evaluation harnesses
• Prompt RAG and agent validation tooling prompt testing frameworks retrieval accuracy validators agent workflow evaluators
• Responsible AI and model risk tooling Fairlearn SHAP Explainable AI libraries toxicity and bias scanners
• Security and adversarial testing tools for AI systems PyRIT Garak
• AI red teaming and threat simulation frameworks automated red team scripts adversarial test suites for LLMs and agents
• AI assurance automation and QE frameworks Galileo
• Observability for AI behavior and drift Langfuse Arize Evidently custom telemetry dashboards
• Client Orientation Leadership
• Partner with product and engineering teams to identify AI Assurance opportunities and shape roadmaps
• Support client workshops RFPs and solution presentations
• Mentor engineers on AI ML Gen AI best practices and emerging technologies
• Translate complex AI concepts into business friendly narratives

Technical Requirements:

• Must Have Qualifications
• 13 years of experience in software engineering with 3 years in AI with strong architecture ownership
• Hands on expertise in AI ML systems LLM evaluation and assurance frameworks
• Experience with AI red teaming model risk management or AI audit tooling
• Strong understanding of Responsible AI AI risks and governance principles
• Experience with security testing adversarial testing and reliability engineering
• Proficiency in Python automation frameworks and cloud platforms
• Good to Have Skills
• Knowledge of regulatory or compliance considerations for AI systems
• Exposure to performance engineering chaos engineering or resilience testing for AI
• Contributions to internal platforms frameworks or standards

Preferred Skills:

Technology->AI-AI Engineering->LLMOps,Foundational->Project Management->Request For Proposal (RFP)/Proposal Development,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->traditional ai ml,Technology->Testing Technologyes->Test Automation Technology,Technology->Agile Testing->Agile Testing - ALL->CD/CI,Technology->AI-Generative AI->Conversational AI Platform,Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag),Technology->Automated Testing->Automated Testing - ALL