Role & responsibilities:

AI Assurance Architecture

- Architect platforms and frameworks for AI assurance, evaluation, and benchmarking

- Design systems for LLM, agent, and RAG evaluation across functional, nonfunctional, 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 redteaming, threat simulation, and chaosstyle 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 redteaming and threat simulation frameworks (automated redteam 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

Preferred candidate profile:

MustHave Qualifications

- 13+ years of experience in software engineering with 3+ years in AI with strong architecture ownership

- Handson expertise in AI/ML systems, LLM evaluation, and assurance frameworks

- Experience with AI red teaming, model risk management, or AI audit tooling

- Robust 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

GoodtoHave 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

📌 AI Trust & Governance Architect - Pan India (Bengaluru)
🏢 Infosys
📍 Bengaluru