VP of Software Engineering (Edge to Cloud)

Overview

As the VP of Engineering, you will lead a multidisciplinary, geographically distributed engineering organization responsible for powering business-critical customer journeys, scalable platforms, core architecture, front-end experience, and AI-driven features. You will shape the long-term technology vision while ensuring short-term delivery excellence and cross-functional alignment. This is a highly visible leadership role that will work closely with Product, Data, Design, GTM, and Executive teams. You’ll drive strategy and execution across both cloud and embedded environments, shaping the technology roadmap in alignment with customer and business priorities. This is a pivotal role in a late-stage, high-growth startup, requiring strong technical depth, cross-functional leadership, and a bias for action.

Responsibilities

1. Strategic & Technical Leadership · Lead and scale high-performing teams across Journey Engineering, Platform, Front-End, and AI/ML.

· Define and execute technology strategy in partnership with Product, Data, and Executive leadership. · Align engineering initiatives to business priorities and product OKRs. · Establish architectural principles that drive agility, security, and sustainable scale.

 

2. Platform, Front-End & Architecture · Drive the evolution of a modern, API-first, microservices-based platform. · Ensure high availability, reliability, and extensibility of internal and customer-facing systems. · Oversee front-end frameworks and design systems to deliver cohesive, performant user experiences. · Reduce tech debt and complexity through architectural governance and modernization. · Optimize systems for observability, performance, and cloud cost efficiency.

 

3. AI/ML and Embedded Systems · Lead practical AI/ML applications across business workflows and customer journeys. · Guide integration of NLP, computer vision, and predictive models in both cloud and embedded environments. · Drive embedded AI delivery with on-device inference, model optimization (quantization/pruning), and edge analytics. · Partner with Hardware, Firmware, and Edge teams to deliver performant, resource-aware AI experiences. · Manage the lifecycle of deployed models with a focus on reliability, ethical use, and real-world value.

 

4. Customer Journey Engineering · Enable journey teams (e.g., onboarding, engagement, support) with shared services, frameworks, and scalable APIs. · Promote reusable design patterns and technical accelerators to drive speed and consistency. · Define metrics to assess impact on customer experience and business KPIs.

 

5. Engineering Excellence & Operational Execution · Foster a culture of accountability, continuous improvement, and technical excellence. · Scale agile delivery processes to improve velocity, test coverage, and deployment frequency.

 

6. Talent & Culture · Attract, develop, and retain world-class engineering talent across disciplines. · Build a diverse, inclusive, and transparent culture rooted in psychological safety and innovation. · Develop strong career paths, mentoring frameworks, and peer-based learning to nurture future leaders

 

 

Qualifications

• Experience in late-stage startup or PE-backed growth environments.
• Strong background in embedded systems, edge computing, or AI-based automation.
• Familiarity with compliance, multi-tenant SaaS architecture, and scalable security frameworks.
• Drive intelligence to existing and future Edge devices
• Developer productivity, DORA. · Feature innovation velocity.
• Creating new revenue streams · Leading a high performing team
• Proven Technology Leadership: 12+ years in engineering leadership roles, including 5+ years at the VP or Sr. Director level, with a strong record of scaling teams in high-growth, global environments (preferably B2B SaaS or platform-based businesses).
• Full-Stack Technical Depth: Strong expertise across cloud-native architecture, modern front-end frameworks (e.g., React), scalable APIs, distributed systems, and real-time data pipelines.
• AI/ML and Embedded Innovation: Hands-on experience delivering production-ready AI/ML solutions, including embedded AI, on-device inference, and self-healing edge-to-cloud architectures for safety-critical and data-rich environments. ·
• ML Ops & Computer Vision: Deep knowledge of AI/ML lifecycles, large-scale data operations, and CV-based applications, with experience building and managing robust ML Ops pipelines.
• Platform Architecture & DevOps: Demonstrated success designing microservices-based, containerized, and orchestrated systems optimized for performance, security, cost, and compliance.
• Journey-Centric Engineering: Experience leading customer journey-focused teams, translating complex needs into modular, scal