Infosys—AI Trust and Governance Architect

Job description

Description: 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 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 Skills: 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