AI Trust and Governance Architect

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

Education

Bachelor of Engineering