Specialist - Data Sciences

Specialist - Data Sciences

Job Title AI Assurance Engineer

Job Summary

We are seeking an AI Assurance Engineer responsible for validating testing governing and assuring the quality safety reliability and compliance of AI systems throughout their lifecycle The role focuses on Responsible AI RAI AI Testing Model Validation Risk Assessment Compliance and AI Governance to ensure AI solutions are trustworthy explainable secure and aligned with organizational and regulatory requirements AI assurance activities include AI audits risk assessments compliance monitoring fairness testing safety validation governance controls and quality evaluation

Key Responsibilities

AI Testing Assurance

Design and execute test strategies for AIML and Generative AI systems

Validate AI model performance accuracy robustness reliability and consistency

Develop test cases for AI applications LLMs chatbots copilots and agentic systems

Perform functional regression integration performance and security testing of AI solutions

Conduct adversarial testing prompt injection testing and robustness assessments

Verify AI system outputs against business ethical and regulatory requirements

Responsible AI Compliance

Assess AI systems for fairness bias explainability transparency and accountability

Support compliance with AI governance frameworks ethical guidelines and industry standards

Participate in AI risk assessments impact assessments and governance reviews

Identify and mitigate AIrelated risks including privacy security and compliance concerns

Validate Responsible AI controls guardrails and human oversight mechanisms

AI Governance Risk Management

Support AI audits assessments and assurance reviews

Maintain AI testing evidence compliance documentation and assurance reports

Contribute to AI governance processes policies and quality frameworks

Monitor AI models for drift hallucinations safety issues and operational risks

Collaborate with AI Governance Compliance Data Science Engineering and Risk teams

Quality Engineering for AI

Define AI quality metrics benchmarks and acceptance criteria

Develop automated AI testing frameworks and validation pipelines

Validate data quality lineage provenance and model traceability

Support continuous monitoring and assurance throughout the AI lifecycle

Establish testing approaches for LLMs RAG solutions AI agents and machine learning models

Required Skills

Strong understanding of

AIML Lifecycle

Generative AI LLMs

Responsible AI Principles

AI Risk Management

AI Governance Frameworks

Experience in

Functional Automation Testing

Model Validation

AI Quality Assurance

Security Compliance Testing

API Testing

Knowledge of

ISOIEC 42001

NIST AI RMF

AI Risk Management

Explainability and Fairness Testing

AI Audit Assurance Practices

Preferred Skills

Experience with

LLM Evaluation Frameworks

Prompt Testing

AI Red Teaming

Responsible AI Platforms

MLOpsLLMOps

Familiarity with

Azure AI

OpenAI

Databricks

MLflow

Python

Automation Frameworks

Understanding of AI Safety and Governance Controls