• Design and implement QA foundations and evaluation frameworks for assessing AI agents, their workflows and processes. • Design and run experiments to assess AI-driven QA processes, including prompt strategies, automation workflows, and agent performance. • Benchmark systems across multiple engineering streams to determine accuracy, reliability, cost, and consistency of outputs. • Develop approaches for creating synthetic datasets and structured test data to support reliable evaluation. • Identify gaps in existing testing approaches and design AI-based solutions or agents to address them. • Build proof-of-concepts (POCs) to explore new testing approaches and validate ideas. • Structure and document product knowledge, including user journeys, APIs, and business rules, to support AI-assisted testing systems. • Evaluate the cost, ROI, and effectiveness of different QA approaches and automation strategies. • Collaborate closely with the QA Lead and engineering teams to integrate AI-assisted QA into development workflows. • Ensure AI-driven testing processes produce consistent, reliable, and high-quality results. • Remain hands-on in building tooling, experiments, and testing infrastructure.

• Work as part of a skilled, multi-disciplined product engineering team to deliver modern software. • Deliver TypeScript services built on Docker, Node, Express, PostgreSQL, and TypeORM. • Contribute to React applications which rely on our APIs and integrate with Shopify. • Deliver features that address real customer problems with your team. • Support Customer Success by maintaining the live production system. • Participate in architectural discussions and contribute to process improvements.

• Spearhead the design and deployment of a next-generation, network device automation infrastructure. • Lead a team in building a platform that automates everything from Pull Request to Regression Validation on physical test beds using high-performance IXIA traffic generators. • Build and deploy AI/LLM-based agents to parse complex log files and SPyTest results. • Develop agents capable of test bed failure recovery and leverage AI to analyze long-term software quality trends.

• Lead the design, implementation, and optimization of Zscaler solutions (ZIA, ZPA) across a complex enterprise environment. • Act as a technical SME for secure network architecture, including zero trust principles and secure access transformation. • Partner with infrastructure, security, and application teams to ensure seamless integration of Zscaler into existing systems and workflows. • Provide hands-on support during rollout, including configuration, troubleshooting, and performance tuning. • Advise engineering teams on secure coding practices, with a focus on risks introduced by AI-assisted development tools (e.g., code generation, copilots). • Conduct and support AI-driven code reviews, identifying vulnerabilities, insecure patterns, and data exposure risks. • Develop security guardrails, standards, and best practices for AI-enabled software development. • Translate security and technical concepts into clear guidance for both technical and non-technical stakeholders. • Support compliance and risk mitigation efforts related to application security and enterprise security controls.