AI Architecture Lead macOS Forensics at SUMURI LLC – Magnolia, Delaware

JOB DESCRIPTION

AI Architecture Lead – macOS Forensics

Location: Remote (US Preferred)

Company: SUMURI LLC – Magnolia, Delaware

Reports To: Founder / Director of Software

Product Focus: RECON ITR & RECON LAB (macOS-native forensic tools)

About SUMURI

SUMURI is a Delaware-based digital forensics company specializing in macOS forensic

software and hardware used by law enforcement, military, and corporate investigators

worldwide. Our flagship tools — RECON ITR (imaging & triage) and RECON LAB (analysis &

reporting) — are undergoing a modern Swift-native rebuild designed for Apple Silicon and

long-term AI integration.

We are building the most advanced macOS forensic AI platform in the world.

Position Summary

The AI Architecture Lead will design and oversee the long-term AI and ML architecture for

RECON 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 standards

This is not a web AI role.

This is not a prompt-engineering role.

This is a macOS-native forensic AI systems architecture role.

Core Responsibilities

1. 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-style

labeling)

● Define standards for pretrained model integration (no custom model training required

initially)

● Ensure deterministic, explainable AI workflows suitable for court testimony

2. 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 requirements

3. 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 generation

4. 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 scrutiny

5. 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 processing

6. 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 requirements

Required Qualifications

Technical

● 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 codebases

AI / 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 outputs

Forensic or High-Security Environment Experience (Preferred)

● Experience in digital forensics

● Experience in cybersecurity

● Experience building tools used in regulated environments

● Understanding of evidentiary handling principles

Nice-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 internals

What 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 vendors

Compensation

Competitive, based on experience.

Equity discussion possible for exceptional candidates.