AI Architecture Lead macOS Forensics

JOB DESCRIPTIONAI Architecture Lead - macOS ForensicsLocation: Remote (US Preferred)Company: SUMURI LLC - Magnolia, DelawareReports To: Founder / Director of SoftwareProduct Focus: RECON ITR & RECON LAB (macOS-native forensic tools)About SUMURISUMURI is a Delaware-based digital forensics company specializing in macOS forensicsoftware and hardware used by law enforcement, military, and corporate investigatorsworldwide. Our flagship tools - RECON ITR (imaging & triage) and RECON LAB (analysis &reporting) - are undergoing a modern Swift-native rebuild designed for Apple Silicon andlong-term AI integration.We are building the most advanced macOS forensic AI platform in the world.Position SummaryThe AI Architecture Lead will design and oversee the long-term AI and ML architecture forRECON ITR and RECON LAB, ensuring:• Native Swift/macOS integration• Apple Silicon optimization• Offline AI model execution• Forensic defensibility• Scalable feature velocity using AI coding agents• Strict privacy and security standardsThis is not a web AI role.This is not a prompt-engineering role.This is a macOS-native forensic AI systems architecture role.Core Responsibilities1. AI Architecture Strategy• Design a long-term AI integration roadmap for RECON LAB and RECON ITR• Architect modular AI pipelines (OCR, face detection, object detection, CLIP-stylelabeling)• Define standards for pretrained model integration (no custom model training requiredinitially)• Ensure deterministic, explainable AI workflows suitable for court testimony2. macOS & Swift Integration• Architect AI features using:• Swift• SwiftUI / AppKit• Core ML• Metal (if needed)• Optimize for Apple Silicon (M-series)• Convert PyTorch / ONNX models into Core ML where appropriate• Ensure compatibility with macOS notarization and sandboxing requirements3. AI Coding Agent Management• Design workflows for:• Using LLM coding agents safely• Automated code validation pipelines• Preventing hallucinated unsafe logic• Enforcing architectural consistency• Build structured AI-assisted development pipelines• Implement guardrails for secure code generation4. Forensic Integrity & Defensibility• Ensure:• AI outputs are logged and reproducible• Chain of custody is preserved• Processing is transparent and reviewable• No cloud dependency unless explicitly configured• Design AI workflows that withstand Daubert/Frye scrutiny5. Performance & Security• Architect offline-first inference pipelines• Ensure no unintended data exfiltration• Implement sandboxed model execution• Optimize inference performance for:• 16GB, 32GB, 64GB Apple Silicon systems• Reduce memory overhead in large case processing6. Leadership• Lead small AI engineering team• Review Swift and ML code for production quality• Mentor developers transitioning from C++/QT to Swift• Collaborate with external development partners• Set coding standards and documentation requirementsRequired QualificationsTechnical• 7+ years professional software engineering experience• 3+ years production Swift development• Deep experience building macOS native applications• Experience integrating ML models into native applications• Experience converting models (PyTorch / ONNX → Core ML)• Strong understanding of:• Apple Silicon architecture• Memory optimization• Concurrency (GCD, async/await)• Security best practices• Experience managing large codebasesAI / ML Experience• Experience implementing:• Object detection (YOLO-style)• OCR pipelines• Face detection & embedding comparison• CLIP-style zero-shot classification• Experience deploying pretrained models (not necessarily training them)• Familiarity with:• Core ML• ONNX Runtime• PyTorch• Vision framework• Understanding of deterministic vs probabilistic outputsForensic or High-Security Environment Experience (Preferred)• Experience in digital forensics• Experience in cybersecurity• Experience building tools used in regulated environments• Understanding of evidentiary handling principlesNice-to-Have (But Not Required)• Experience testifying or supporting expert testimony• Experience building offline AI systems• C++ interoperability knowledge• Metal acceleration knowledge• Experience building CLI forensic tools• Experience with APFS / macOS internalsWhat Success Looks Like (12-24 Months)• RECON LAB has modular AI engine framework• All AI runs offline by default• AI coding agents reduce feature development time by 40%+• No AI-related architectural rewrites required• Clean Swift-native codebase• Clear AI audit logging system• Production-ready model update pipeline• Competitive advantage over SaaS-only forensic vendorsCompensationCompetitive, based on experience.