Usage of Conventional AI
Virtusa utilizes Artificial Intelligence (AI) technology for the purpose of conducting candidate pre-employment assessments. This AI may include both text-based and voice-based functionalities. The AI technology will have access to the data that you upload or provide during your interaction with the AI bot and will use such data solely for the purpose of performing assessment in accordance with Virtusa policies and practices. The AI is not used to substantially assist or replace discretionary employment decisions. Read our full employee privacy policy here.
Job description
Key Result Areas (Responsibilities)
AI/ML Model Testing: Design and execute testing strategies for Large Language
Models (LLMs), RAG (Retrieval-Augmented Generation) architectures, and
predictive models. Focus on model accuracy, hallucination detection, and relevance
scoring.
Test Automation Frameworks: Build and maintain automated testing frameworks
(using Python, PyTest, or similar) specifically for data pipelines and ML inference
layers.
Bias & Ethical AI Assurance: Conduct rigorous testing to identify bias, fairness
issues, and safety vulnerabilities in AI outputs, ensuring compliance with responsible
AI governance frameworks.
Data Integrity & Quality: Validate the quality of training and test datasets. Perform
SQL-based data verification and ensure data privacy standards (PII protection) are
met during the testing lifecycle.
Performance & Scalability: execute load testing on AI model endpoints (APIs) to
ensure low latency and high availability under peak banking transaction volumes.
Collaboration & Reporting: Work as a bridge between the Data Science and
DevOps teams. articulate complex AI defects to stakeholders and produce detailed
quality reports using tools like Jira, Confluence, or Power BI.
Knowledge, Skills, and Experience
Core Experience: 7–8 years of total experience in Software Quality Assurance,
with at least 3+ years dedicated to AI/ML or Data testing.
Technical Proficiency:
o Strong programming skills in Python (mandatory for scripting and
automation).
o Experience testing Generative AI/LLM applications (e.g., validating prompt
engineering, checking vector DB retrieval accuracy).
o Proficiency in SQL and working with large datasets.
o Familiarity with cloud platforms (Azure or AWS) and MLOps tools.
Domain Knowledge: Prior experience in the Banking/FinTech domain is highly
preferred (specifically in areas like KYC, AML, or Customer Service Automation).
Soft Skills: Excellent analytical thinking, ability to work in a fast-paced agile
environment, and strong communication skills to interact with onshore
stakeholders.
Educational Qualifications
Bachelor’s Degree in Computer Science, Engineering, or a related quantitative
field.
Certifications in AI/ML (e.g., Azure AI Engineer, ISTQB AI Testing) are a plus.
Qualification
Key Result Areas (Responsibilities)
AI/ML Model Testing: Design and execute testing strategies for Large Language
Models (LLMs), RAG (Retrieval-Augmented Generation) architectures, and
predictive models. Focus on model accuracy, hallucination detection, and relevance
scoring.
Test Automation Frameworks: Build and maintain automated testing frameworks
(using Python, PyTest, or similar) specifically for data pipelines and ML inference
layers.
Bias & Ethical AI Assurance: Conduct rigorous testing to identify bias, fairness
issues, and safety vulnerabilities in AI outputs, ensuring compliance with responsible
AI governance frameworks.
Data Integrity & Quality: Validate the quality of training and test datasets. Perform
SQL-based data verification and ensure data privacy standards (PII protection) are
met during the testing lifecycle.
Performance & Scalability: execute load testing on AI model endpoints (APIs) to
ensure low latency and high availability under peak banking transaction volumes.
Collaboration & Reporting: Work as a bridge between the Data Science and
DevOps teams. articulate complex AI defects to stakeholders and produce detailed
quality reports using tools like Jira, Confluence, or Power BI.
Knowledge, Skills, and Experience
Core Experience: 7–8 years of total experience in Software Quality Assurance,
with at least 3+ years dedicated to AI/ML or Data testing.
Technical Proficiency:
o Strong programming skills in Python (mandatory for scripting and
automation).
o Experience testing Generative AI/LLM applications (e.g., validating prompt
engineering, checking vector DB retrieval accuracy).
o Proficiency in SQL and working with large datasets.
o Familiarity with cloud platforms (Azure or AWS) and MLOps tools.
Domain Knowledge: Prior experience in the Banking/FinTech domain is highly
preferred (specifically in areas like KYC, AML, or Customer Service Automation).
Soft Skills: Excellent analytical thinking, ability to work in a fast-paced agile
environment, and strong communication skills to interact with onshore
stakeholders.
Educational Qualifications
Bachelor’s Degree in Computer Science, Engineering, or a related quantitative
field.
Certifications in AI/ML (e.g., Azure AI Engineer, ISTQB AI Testing) are a plus.
Key job details
About Virtusa
Teamwork, quality of life, professional and personal development: values that Virtusa is proud to embody. When you join us, you join a team of 30,000 people globally that cares about your growth — one that seeks to provide you with exciting projects, opportunities and work with state of the art technologies throughout your career with us.
Great minds, great potential: it all comes together at Virtusa. We value collaboration and the team environment of our company, and seek to provide great minds with a dynamic place to nurture new ideas and foster excellence.
Virtusa is an Equal Opportunity Employer. All applicants will receive fair and impartial treatment without regard to race, color, religion, sex, national origin, ancestry, age, legally protected physical or mental disability, protected veteran status, status in the U.S. uniformed services, sexual orientation, gender identity or expression, marital status, genetic information or on any other basis which is protected under applicable federal, state or local law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government-issued ID during each interview. All candidates must be authorized to work in the USA.
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