AI Architecture Lead macOS Forensics

SUMURI LLC•Magnolia, DE

10h•Remote

About The Position

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.

Requirements

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
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

Nice To Haves

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

Responsibilities

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
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
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
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
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
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