Principal Software Engineer - Mobile SDK Tooling & On‑Device Computer Vision (iOS/Android)

ROLE •    Strengthen observability and diagnostics for SDKs and reference apps using Splunk and other tools (logs, metrics, crash signals, field diagnostics).  •    Build and own engineering tooling and automation, and define a multi-quarter platform roadmap, to measurably reduce operational workload and increase developer productivity at scale across the team (~20 engineers), while reducing integration and support effort. •    Design, implement, and maintain Continuous Integration / Continuous Delivery pipelines and quality gates (Jenkins) for mobile SDKs (build, test, packaging, release validation, regression detection).  •    Develop release qualification frameworks: automated functional tests, performance benchmarks, compatibility checks, and reproducible test environments.  •    Deliver and evolve core components of mobile SDKs (iOS, Android): camera capture, image processing pipelines, API design, modularization, documentation, smartphone apps.  •    Provide technical leadership as an Individual Contributor (no management): architecture decisions, design reviews, mentoring through code reviews, and raising engineering standards. •    Lead the integration of AI/ML into the mobile detection stack, from rapid prototyping to production-grade deployment on iOS/Android. •    Build an AI-driven validation and triage layer for SDK releases, including automated regression and anomaly detection. PROFILE •    Principal Engineer with a strong track record of delivering production software. •    Proven experience building developer tooling / automation platforms that measurably improve team productivity (e.g., faster releases, fewer regressions, reduced support load). •    Strong iOS expertise: Swift, Xcode, SDK design and packaging, performance profiling (Instruments); Objective C is a plus. •    Solid Android foundations: Kotlin/Java, Gradle, CameraX/Camera2, instrumentation/testing; ability to support and debug across both platforms. •    Strong understanding of CI/CD concepts and implementation experience with Jenkins (pipelines, build/test orchestration, quality gates, artifacts, release processes). •    Strong knowledge of software architecture, API design, testing strategy, secure coding practices, and maintaining high-quality codebases. •    Hands-on experience building and optimizing computer vision / image processing pipelines on mobile devices and under real-world constraints (latency, memory, device variability). •    Strong problem‑solving skills, translating customer needs into scalable solutions. •    Hands‑on experience deploying AI/ML models to production on resource‑constrained, heterogeneous devices. •    Solid expertise in the full ML lifecycle, including data quality, evaluation, robustness, and model release management. •    Practical experience with on-device ML toolchains and runtimes and performance engineering under real device constraints. •    Ability to define and enforce AI usage standards in an industrial…