Associate Vice President - Quality Engineer Specialist (AI)
Role Overview:
As a Quality Engineer Specialist Leader specializing in artificial intelligence (AI) and Generative AI (GenAI) technologies, you will actively engage in your quality engineering craft and be the visionary driving force behind our transformation to AI-powered quality engineering, taking a hands-on approach to multiple high-visibility projects. Your expertise will be pivotal in delivering solutions that delight customers and users while driving tangible value for Deloitte's business investments. Leveraging your extensive quality engineering and AI craftsmanship, along with advanced proficiency across multiple quality assurance disciplines and modern AI/GenAI frameworks and models, you will consistently demonstrate an exemplary track record in delivering high-quality, outcome-focused solutions. The ideal candidate will have a strong background in quality assurance, test automation, and a deep understanding of AI and GenAI technologies, including their application to quality engineering. This role is crucial in enhancing our testing frameworks and ensuring the highest quality standards for our products.
The team
US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost-effective model that focuses on value/outcomes that leverages a progressive and responsive talent structure. As Deloitte’s primary internal development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations with proven bottom-line results and outcomes. It helps power Deloitte’s success. It is the engine that drives Deloitte, serving many of the world’s largest, most respected companies. We develop and deploy cutting-edge internal and go-to-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence.
Key Responsibilities:
- Strategic Vision and Alignment: Accountable for crafting and articulating a vision for AI/GenAI as it specifically applies to quality engineering in alignment with the US Deloitte Technology GenAI strategy. Collaborate with diverse stakeholders, including product, engineering, experience, delivery, security, and infrastructure teams across various organizational levels.
- Advocacy and Technology Roadmap: Advocate for, develop, and communicate the AI/GenAI quality engineering implementation approach to the product engineering teams. Ensure the organization is well-informed about objectives, KPIs, technology roadmaps, and progress. Focus on reuse and leverage of existing quality assets to minimize overall costs with an eye on continuous improvement.
- Craft Mastery and Objectives Realization: Define, measure, and drive the achievement of KPIs related to quality approaches, automation tools, test data management (e.g., subsetting, masking, generation, virtualization, anonymization), and standards, ensuring they are fit-for-purpose in product testing cycles. Implement advanced techniques such as BDD and GenAI-based automation to evolve testing frameworks and increase automation coverage, minimizing manual quality assurance to exceptional scenarios only. Establish and refine AI/ML/GenAI domain standards and best practices. Remain hands-on with test design and scripting/coding automations, contributing to team velocity during sprints, and actively engage with engineers across the SSDLC. Be self-driven to learn new technologies, experiment with them, and inspire quality engineers to apply new technologies effectively.
- Capability Evolution and Development: Mentor and develop quality engineers to become masters of their craft. Coach and develop their skills in modern engineering practices, specifically related to AI/ML/GenAI. Showcase learning and mastery by showcasing experiments internally, speaking at conferences, writing whitepapers or blogs, and leading R&D collaborations with academia and communities.
- Iterative Value Delivery: Embrace an iterative/incremental approach to quality engineering. Apply a leaning-forward approach to navigate complexity and uncertainty. Ensure alignment with customer and business goals through iterative steps and empirical evidence.
- Customer-Centric Problem Solving: Focus on addressing critical issues faced by customers and users. Align technical solutions with business objectives. Minimize unnecessary technical complexities and overengineering. Drive teams toward peak performance through continuous learning and improvement.
- Expert Proficiency and Continuous Improvement: Possess deep expertise in modern software engineering and quality engineering practices. Identify inefficiencies and opportunities for innovation. Act as a role model and enhance the product engineering operating model to be lean, adaptable, and responsive to changes, ensuring that quality engineering teams can deliver business value efficiently and effectively. Guide and transform the organization to embrace lean principles and foster a culture of innovation.
- Tech/Quality Risk Management: Ensure appropriate quality tool use, adoption by quality engineers, and confirm/review that products are tested, scaled, and operated to be free of defects and are secure/compliant, including providing guidance for necessary optimizations. Identify potential quality risks and develop mitigation strategies. This involves proactive problem-solving and contingency planning to address any issues that may arise during development.
- Influential Communication: Influence, persuade, and drive decision-making processes. Communicate effectively in both written and verbal forms. Craft clear, structured arguments and technical trade-offs supported by evidence.
- Organizational Engagement and Collaboration: Engage stakeholders at all levels of the organization, from team members to middle management to executives. Build collaborative and constructive relationships. Co-create and drive momentum and value across multiple organizational levels.
Key Qualifications:
- A bachelor’s degree in computer science, software engineering, or a related discipline. An advanced degree (e.g., MS) is preferred but not required. Experience is the most relevant factor.
- 15+ years of hands-on experience in quality assurance/engineering, test automation including coding the test scripts, and in AI/ML, with last 2 years focused on GenAI as well.
- Proven experience in Python, TensorFlow, PyTorch, Keras, C#, Julia, and ML libraries.
- Strong experience in GenAI models and technologies such as OpenAI, Claude, Gemini, LangChain, Vector databases, and approaches like prompt engineering, fine-tuning, etc.
- Hands-on experience with automation-first techniques like BDD and test automation tools such as TOSCA, Selenium, Gherkin, Functionize, Testim, and AccelQ.
- Strong experience with cloud hyper-scalers like AWS, Azure, and GCP.
- Expertise in modern software engineering practices, including Agile methodologies, DevSecOps, SRE, MLOps, and deployment techniques like Blue-Green, and Canary to enable A/B testing approaches.
- Proven track record of leading and managing high-performing quality engineering teams.
- Exceptional interpersonal and organizational skills as well as strategic mindset with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.
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