Principal Engineer – Mobile SDK Tooling & On‑Device Vision (iOS/Android)

About the role

We are looking for a Principal Software Engineer to lead the development of mobile SDK tooling and on‑device computer‑vision capabilities for iOS and Android. The role focuses on improving observability, automating CI/CD pipelines, and integrating AI/ML models into the detection stack while mentoring a team of engineers.

Key responsibilities

• Strengthen SDK observability using Splunk, logs, metrics, crash signals and field diagnostics.
• Design, build and own engineering tooling and automation, defining a multi‑quarter roadmap to reduce operational workload and boost developer productivity for a ~20‑engineer team.
• Create and maintain CI/CD pipelines and quality gates in Jenkins for build, test, packaging, release validation and regression detection.
• Develop release qualification frameworks including automated functional tests, performance benchmarks, compatibility checks and reproducible test environments.
• Deliver core SDK components such as camera capture, image‑processing pipelines, API design, modularization and documentation for iOS (Swift/Objective‑C) and Android (Kotlin/Java).
• Provide technical leadership as an individual contributor: architecture decisions, design reviews, code‑review mentorship and raising engineering standards.
• Lead AI/ML integration from rapid prototyping to production deployment on mobile devices, building validation and triage layers with automated regression and anomaly detection.

Required profile

• Principal‑level engineer with a proven record of shipping production software.
• Experience building developer tooling or automation platforms that measurably improve release speed and quality.
• Deep expertise in iOS (Swift, Xcode, Instruments) and solid Android knowledge (Kotlin/Java, Gradle, CameraX/Camera2).
• Strong background in CI/CD, especially Jenkins pipeline creation and management.
• Hands‑on experience with computer‑vision and image‑processing pipelines on mobile devices.
• Proficiency deploying AI/ML models on resource‑constrained, heterogeneous devices and managing the full ML lifecycle.
• Excellent problem‑solving and communication skills in multicultural environments.

Required skills

• Splunk
• Jenkins
• CI/CD pipelines
• Swift
• Objective‑C
• Xcode
• Kotlin
• Java
• Gradle
• CameraX
• Camera2
• Computer vision
• Image processing
• AI/ML integration
• On‑device ML toolchains
• Performance profiling (Instruments)