AI Trust and Governance Architect

Infosys; Bengaluru; On-site; INR 1,600,000 - 2,400,000; Full time

Job summary

Infosys in Bengaluru is seeking a seasoned AI Assurance Architect to design and implement evaluation frameworks and AI assurance platforms. The role involves leading integration with CI/CD pipelines, collaborating with engineering teams, and mentoring staff on AI and ML best practices.

The ideal candidate will have over 13 years of experience in software engineering, including extensive knowledge of AI/ML systems. A Bachelor's degree in Engineering is required, along with proficiency in Python and automation frameworks. Contributions to AI governance and risk management are essential.

Qualifications

• Experience in software engineering for 13+ years, with 3+ years in AI.
• Knowledge of AI red teaming and model risk management.
• Proficient in automation frameworks and cloud platforms.

Responsibilities

• Architect platforms for AI assurance and evaluation.
• Enable integration with CI/CD pipelines for AI quality.
• Mentor teams on AI risk identification and mitigation.

Skills

Experience in AI/ML systems

Architecture ownership

Hands-on expertise in LLM evaluation

Proficiency in Python

Education

Bachelor of Engineering

Job description

Educational Requirements

Bachelor of Engineering

Service Line

Infosys Quality Engineering

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 ReliabilitySecurity, 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 and Professional 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

Preferred Skills

• Foundational->Governance_COBIT->Assurance
• Domain->Information Security->Governance Risk and Compliance
• Audits
• Workflow
• Technology->Consulting - Data Governance->Data Governance
• Technology->AI-Generative AI->Conversational AI Platform
• Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag)
• Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->traditional ai ml
• Technology->AI-Responsible AI->Responsible AI
• Technology->AI-Generative AI->Prompt Engineering