VP OF Engineering

VP of Engineering

Job Title: VP of Engineering

Company: Matilda Cloud

Location: Richardson, TX (Hybrid)

About Matilda Cloud: Matilda Cloud is at the forefront of AI‑driven cloud management, delivering advanced solutions that simplify complex cloud operations, from multi‑cloud assessments and seamless migrations to comprehensive optimization and modernization. Our platform integrates machine learning and real‑time data insights to automate traditionally resource‑intensive processes, enabling enterprises to scale their cloud environments efficiently and securely. By leveraging a modular yet interconnected suite of tools, we provide granular visibility and control over cloud infrastructure, helping businesses not only reduce costs but also meet stringent security, compliance, and performance standards.

A central mandate of this role is establishing engineering excellence as a core organizational discipline. This is not a refinement project; it is a greenfield build. Specifically, you will be expected to design, scale, and institutionalize engineering practices.

• Lead, mentor, and grow a distributed team of engineers and QA professionals across Texas and India, fostering a high‑performance, inclusive culture.
• Build organizational structures, career ladders, and performance frameworks that scale with the company’s growth.
• Partner with recruiting to attract senior engineering talent and reduce reliance on any single point of knowledge.
• Serve as a visible, accessible leader who earns trust at every level of the engineering organization.

• Own the end‑to‑end transformation of engineering practices from informal, relationship‑driven workflows to a predictable, repeatable, and scalable operating model that can support continued headcount and product growth.
• Design and implement a mature SDLC: structured sprint ceremonies, definition‑of‑ready and definition‑of‑done standards, disciplined backlog management, and a clear path from feature request to production release.
• Establish engineering‑wide standards for code quality including mandatory code review, branching strategies, test coverage thresholds, documentation requirements, and build the culture of accountability that makes those standards stick.
• Build or significantly mature CI/CD pipelines, automated testing infrastructure, and release management processes that enable confident, frequent deployments with reduced risk.
• Introduce and institutionalize engineering metrics (cycle time, lead time, defect escape rate, deployment frequency) to create visibility into team health and delivery performance and use that data to drive continuous improvement.
• Balance rigor with practicality, implementing process improvements incrementally, with clear rationale, so that professionalization feels like an enabler rather than overhead to the engineering team.

• Establish a forward‑looking engineering culture that embraces AI‑assisted development tooling as a standard part of how the team works including AI code completion, automated code review assistance, test generation, and documentation and build adoption norms that maximize productivity gains while preserving code quality and security standards.
• Develop and enforce clear policies around AI tool usage in the engineering workflow: what is sanctioned, what is off‑limits given enterprise customer data obligations, and how AI‑generated code is reviewed and validated before it enters production.

• Partner closely with the Director of Product Management to evaluate the technical feasibility of AI‑powered product features, providing honest assessments of data requirements, model complexity, latency constraints, and operational overhead before commitments are made.
• Build and maintain engineering capabilities required to deliver and operate AI features in production: model integration patterns, inference infrastructure, evaluation pipelines, monitoring for model drift, and graceful degradation when AI components underperform.
• Ensure the engineering team develops the…