Senior AI QA Engineer — GenAI & LLM Testing Lead

Tata Consultancy Services; Charlotte (NC); On-site; USD 100,000 - 120,000; Full time

Benefits offered by this job

Discretionary Annual Incentive

Comprehensive Medical Coverage

Family Leave

Identity Theft Protection

Certification & Training Reimbursement

Vacation & Holidays

Job summary

Tata Consultancy Services in Charlotte, NC seeks an experienced QA Automation Lead to design and validate AI/ML test plans, ensuring model accuracy, bias testing, and scalable performance. Responsibilities include leading a team of engineers, building automation frameworks in Python and PyTest, and validating data quality across training and test sets.

The role emphasizes automation, security considerations, and collaboration with data scientists and developers to deliver robust AI solutions.

Qualifications

• Bachelor’s or master’s degree in computer science or related field.
• Minimum 8 years of experience.

Responsibilities

• Lead strategy: design end-to-end test plans for generative AI, LLMs, and ML models.
• Model validation: test models for accuracy, drift, latency, robustness, and security vulnerabilities.
• Automation: build automated test frameworks using Python, PyTest, and AI testing tools.
• Data quality: validate training, validation, and test datasets for bias, leakage, and data integrity.
• Performance testing: evaluate system scalability and response times under heavy concurrent loads.
• Team leadership: mentor junior engineers and collaborate with data scientists and software developers.
• Certifications in Automation

Skills

AI testing

RAG systems

Agents

APIs

Data pipeline

Python

PyTest

Automation tools

Education

Bachelor’s degree in computer science

Tools

Python

PyTest

AI testing tools

Job description

Tata Consultancy Services in Charlotte, NC seeks an experienced QA Automation Lead to design and validate AI/ML test plans, ensuring model accuracy, bias testing, and scalable performance. Responsibilities include leading a team of engineers, building automation frameworks in Python and PyTest, and validating data quality across training and test sets.

The role emphasizes automation, security considerations, and collaboration with data scientists and developers to deliver robust AI solutions.