IN
Job Description
Key Responsibilities:
- Oversee AI-driven technology consulting within the SRE, DevOps, and Data Intelligence sectors, assessing client quality ecosystems to identify modernization opportunities. Develop AI-focused technology strategies and blueprints that embed intelligence into every quality touchpoint, from code commits to production monitoring.
- Facilitate agentic and LLM strategy consulting, performing evaluations and fitment analyses for agentic and LLM technologies. Craft adoption roadmaps for autonomous quality agents and provide guidance on how generative AI transforms test design, defect prediction, and quality workflows.
- Consult on exponential engineering platforms such as Devin Topaz Fabric QoS and other emerging AI-native tools. Formulate adoption strategies for Topaz Fabric QoS, assess platform compatibility, and design integration models that enhance engineering productivity at an enterprise level.
- Conduct scalability analyses for agentic AI quality infrastructure, ensuring that test ecosystems can scale elastically in line with release velocity and application complexity. Generate scalability analysis reports detailing capacity constraints and suggesting investment priorities.
- Engage with DTA-led regulatory and compliance advisory on data sovereignty in testing environments, focusing on the auditability of AI-generated artifacts and traceability for regulated industries. Translate legal requirements into engineering guidelines that foster innovation within regulatory constraints.
- Provide tailored engineering consulting by customizing quality strategies to align with each client’s specific technology landscape, organizational culture, and business context, ensuring that recommendations are practical and implementable rather than merely theoretical.
- Lead short-term value engagements lasting 4 to 8 weeks, from discovery to proof cycles, showcasing measurable AI-enhanced quality outcomes. Convert skepticism into investment commitment through demonstrated business impact.
- Develop the practice’s technological viewpoint by creating vendor-agnostic evaluation frameworks, reference architectures, and adoption playbooks, ensuring clients receive the most suitable recommendations without vendor bias.
Technical Requirements:
- Core QE Solution Architecture
- A minimum of 15 years in Quality Engineering with a career trajectory from hands-on roles to architecture and advisory positions, including at least 4 to 5 years of experience in designing enterprise-scale QE solutions or leading QE technology practices across multiple product lines.
- Proven capability in architecting quality ecosystems that encompass comprehensive end-to-end quality platforms, integrating test intelligence, execution infrastructure, and quality analytics into a unified, scalable system.
- A history of delivering quantifiable quality outcomes, including cycle time reduction, coverage expansion, and defect escape elimination, supported by business metrics rather than solely technical deliverables.
- In-depth knowledge of quality measurement systems that link engineering activities to business indicators, capable of building dashboards for CTOs to present to boards, rather than producing test reports that PMs archive.