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

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