Educational Requirements;
Bachelor of Engineering

Service Line;
- Infosys Quality Engineering

- Responsibilities;
- Key ResponsibilitiesAI Assurance ArchitectureArchitect platforms and frameworks for AI assurance, evaluation, and benchmarkingDesign systems for LLM, agent, and RAG evaluation across functional, non functional, and risk dimensionsDefine architectural patterns for Responsible AI, bias detection, explainability, and safety validationBuild reusable assurance components supporting Business Assurance, Risk Assurance, and ReliabilitySecurity, Reliability GovernanceArchitect AI testing and validation for security, privacy, prompt injection, and adversarial robustnessIntegrate red teaming, threat simulation, and chaos style validation for AI systemsDefine governance mechanisms for model usage, auditability, traceability, and complianceEnsure AI systems meet enterprise standards for resilience, fault tolerance, and observabilityPlatform Engineering EnablementDesign AI assurance platforms supporting automated test execution, reporting, and insightsEnable integration with CI/CD pipelines to enforce AI quality gatesCollaborate with QE engineering teams to embed AI assurance into the SDLCMentor teams on AI risk identification and mitigation from an engineering perspectiveCore Platforms, Frameworks ToolingLLM 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 LeadershipPartner with product and engineering teams to identify AI Assurance opportunities and shape roadmapsSupport client workshops, RFPs, and solution presentationsMentor engineers on AI/ML/Gen AI best practices and emerging technologiesTranslate complex AI concepts into business-friendly narratives
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Technical and Professional Requirements:;
- Must Have Qualifications13+ years of experience in software engineering with 3+ years in AI with solid architecture ownershipHands on expertise in AI/ML systems, LLM evaluation, and assurance frameworksExperience with AI red teaming, model risk management, or AI audit toolingStrong understanding of Responsible AI, AI risks, and governance principlesExperience with security testing, adversarial testing, and reliability engineeringProficiency in Python, automation frameworks, and cloud platformsGood to Have SkillsKnowledge of regulatory or compliance considerations for AI systemsExposure to performance engineering, chaos engineering, or resilience testing for AIContributions to internal platforms, frameworks, or standards

Preferred Skills:;

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

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📌 AI Trust Governance Architect (Bengaluru)
🏢 Infosys
📍 Bengaluru