Job
Description
In this role at PwC, you will be part of the data and analytics engineering team, focusing on utilizing advanced technologies to create robust data solutions for clients. Your responsibilities will include transforming raw data into actionable insights, enabling informed decision-making, and driving business growth. Specifically, in data engineering, you will design and build data infrastructure and systems to facilitate efficient data processing and analysis. Your role will involve developing and implementing data pipelines, data integration, and data transformation solutions. Key Responsibilities: - Design and build data infrastructure and systems for efficient data processing and analysis. - Develop and implement data pipelines, data integration, and data transformation solutions. Qualifications Required: - BE / B Tech / MCA / M Tech - Certified Software Quality Analyst (CSQA), AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, Google Professional Machine Learning Engineer At PwC AC, as an AI Solution QA, you will lead quality assurance practices for AI solutions to ensure they meet high-quality standards and deliver superior value to clients. Your tasks will involve defining and implementing AI-specific QA standards and methodologies, leading a team of QA professionals, developing comprehensive testing frameworks for AI and Machine Learning models, and collaborating with various stakeholders to integrate QA practices into the development lifecycle. Key Responsibilities: - Define, implement, and manage AI-specific QA standards and methodologies. - Lead a team of QA professionals to ensure rigorous testing of AI applications, models, and integrations. - Develop comprehensive testing frameworks for AI and Machine Learning models. - Collaborate with AI architects, developers, data scientists, and stakeholders to integrate QA practices. - Manage risk assessment processes and oversee testing activities. - Provide strategic oversight to ensure compliance with standards and regulatory requirements. - Mentor QA team members, facilitating professional growth. Preferred Knowledge: - Minimum 10-12 years of experience in software QA/testing with at least 3-5 years in AI/ML quality assurance. - Strong understanding of AI/ML concepts, model evaluation, testing techniques, and quality metrics. - Experience with AI and ML tools such as TensorFlow, PyTorch, AWS SageMaker, Azure ML. - Excellent communication, stakeholder management, and problem-solving skills. Additional Company Details: PricewaterhouseCoopers Acceleration Center (PwC AC) is an extension of PwC's global delivery capabilities, providing high-quality technology services for projects based in the United States and global clients. The center focuses on software development, ERP programming, application integration, and support services. In this role at PwC, you will be part of the data and analytics engineering team, focusing on utilizing advanced technologies to create robust data solutions for clients. Your responsibilities will include transforming raw data into actionable insights, enabling informed decision-making, and driving business growth. Specifically, in data engineering, you will design and build data infrastructure and systems to facilitate efficient data processing and analysis. Your role will involve developing and implementing data pipelines, data integration, and data transformation solutions. Key Responsibilities: - Design and build data infrastructure and systems for efficient data processing and analysis. - Develop and implement data pipelines, data integration, and data transformation solutions. Qualifications Required: - BE / B Tech / MCA / M Tech - Certified Software Quality Analyst (CSQA), AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, Google Professional Machine Learning Engineer At PwC AC, as an AI Solution QA, you will lead quality assurance practices for AI solutions to ensure they meet high-quality standards and deliver superior value to clients. Your tasks will involve defining and implementing AI-specific QA standards and methodologies, leading a team of QA professionals, developing comprehensive testing frameworks for AI and Machine Learning models, and collaborating with various stakeholders to integrate QA practices into the development lifecycle. Key Responsibilities: - Define, implement, and manage AI-specific QA standards and methodologies. - Lead a team of QA professionals to ensure rigorous testing of AI applications, models, and integrations. - Develop comprehensive testing frameworks for AI and Machine Learning models. - Collaborate with AI architects, developers, data scientists, and stakeholders to integrate QA practices. - Manage risk assessment processes and oversee testing activities. - Provide strategic oversight to ensure compliance with standards and regulatory requirements. - Mentor QA team members, facilitating professional growth. Preferred Knowledge: -
