IN-Specialist 3__AI-Testing_GCC_Advisory_Bangalore

Advisory

Not Applicable

Data, Analytics & AI

Specialist

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.

In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.

*Why PWC

At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us.

At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. "

Job Description & Summary: A career within…. A career within Data and Analytics services will provide you with the opportunity to help organizations uncover enterprise insights and drive business results using smarter data analytics. We focus on a collection of organizational technology capabilities, including business intelligence, data management, and data assurance that help our clients drive innovation, growth, and change within their organizations in order to keep up with the changing nature of customers and technology. We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge.  

 

Responsibilities: 

 

• Define and implement enterprise-wide AI testing and quality assurance strategies. 

• Drive continuous improvement in test automation, quality metrics, and release processes. 

• Build and maintain scalable automation frameworks for AI applications and APIs. 

• Develop automated validation pipelines for machine learning models. 

• Integrate automated testing into CI/CD workflows. 

• Validate LLM responses using automated evaluation frameworks. 

• Design testing strategies for chatbots, copilots, agents, and RAG applications. 

• Evaluate prompt effectiveness and response quality. 

• Implement automated prompt testing and output validation. 

• Conduct load, stress, scalability, and resilience testing for AI  

 

Mandatory skill sets: 

 

• Playwright 

• Selenium 

• MLflow  

• OpenAI Evaluations 

• CI/CD Pipelines 

 

Preferred skill sets: 

 

• Experience testing LLMs, RAG architectures, AI Agents, and Copilot  

Years of experience required: 

 

0-2 

Education qualification: 

 

B.Tech / M.Tech / MBA / MCA 

Azure Devops

Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, AI Fluency, AI-Human Collaboration, Amazon Web Services (AWS), Apache Airflow, Apache Hadoop, Azure Data Factory, Communication, Data Anonymization, Data Architecture, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Databricks Unified Data Analytics Platform, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling, Data Pipeline {+ 25 more}

Not Specified

No

No

May 31, 2026