AI Trust and 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: Domain->Information Security->Governance Risk and Compliance, Audits , Workflow,Foundational->Governance_COBIT->Assurance,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->traditional ai ml,Technology->AI-Generative AI->Conversational AI Platform,Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag),Technology->AI-Generative AI->Prompt Engineering,Technology->AI-Responsible AI->Responsible AI,Technology->Consulting - Data Governance->Data Governance Skills: Security Testing, Reliability Engineering Experience: 13.00-15.00 Years