Prowler

• Think beyond the code and about our users and customers, understand their needs and define solutions with the team • Collaborate with cross-functional teams to design and develop the backend for the Prowler product • Design, build, and maintain secure and scalable back-end APIs using containerized Django apps and Python • Contribute to backend architecture decisions and product infrastructure planning • Optimize system performance, reliability, scalability and security in a cloud-native environment • Collaborate with product owners, designers, and fellow developers to define project requirements and features meant to cover business needs • Lead code reviews, automated testing, and continuous deployment workflows, providing constructive feedback to maintain high code quality and adhere to best practices • Mentor other engineers on this matter • Lead the continuous improvement of best practices introducing latest industry standards • Manage and integrate relational databases, ensuring consistency, scalability, and performance (currently using PostgreSQL) • Actively participate in Prowler community support, guide others on this matter

• Define the AI Roadmap: Collaborate with founders and Product Leads to translate business goals into a technical AI strategy, prioritizing high-impact capabilities for cloud security. • Architect & Lead Development: Take full ownership of the AI-powered product architecture, from the core LLM orchestration to production deployment and monitoring. • Mentor and Grow the Team: Lead code reviews, establish engineering best practices for ML/AI, and mentor other engineers to foster a culture of technical excellence. • Design Advanced AI Agents: Lead the creation of specialized, autonomous agents for cloud-security tasks, ensuring they operate within strict frameworks of permissions, guardrails, and safety. • Champion Multi-Cloud Integration: Oversee the end-to-end flows from "user intent" to "actionable outcome" across AWS, Azure, GCP, and OCI at an enterprise scale. • Establish Rigorous Evals: Define and maintain the framework for measuring model quality (accuracy, hallucination rates, latency, and cost) and ensure these metrics drive our iteration cycles. • Applied Innovation: Stay ahead of the curve in LLMs/Agents, evaluating new papers and technologies to adopt what is practical and secure, while ignoring the hype.