AI/ML engineering lead and hands-on builder with 12+ years of production engineering across FinTech, trading, agentic systems, and mobile platforms.
I turn ML and LLM prototypes into reliable products: data and evaluation, architecture, deployment, observability, risk controls, and iteration. My strongest evidence sits at the intersection of grounded RAG and AI agents, financial ML, and on-device intelligence.
Open to remote AI/ML Engineering Lead, Applied AI Lead, Head of AI, and ML Engineering Manager roles—especially in FinTech, trading, AI-enabled platforms, and data-intensive products.
AI/ML portfolio · AI/ML resume · LinkedIn · Email
| Project | What it demonstrates | Stack |
|---|---|---|
| LLMCORTEX | Local-first pre-reasoning risk tripwires for coding agents: deterministic matching, composable risk signals, audit logs, PyPI packaging, and 219 passing tests | Python, agents, safety, retrieval |
| GoldGRU | Inspectable financial time-series research with BiGRU/self-attention, 18 selected features, walk-forward validation, and explicit overfitting controls | Python, PyTorch, scikit-learn |
| EvidencePipelineKit | PDFKit/Vision OCR with confidence and provenance, deterministic retrieval, contradiction gates, citations, and explicit human-review outcomes | Swift 6, Vision, PDFKit, grounded RAG |
| FinStreamKit | Production-oriented real-time finance patterns: actor-owned state, stale-event handling, integer money, deterministic risk rules, cancellation, tests, and CI | Swift 6, concurrency, XCTest |
| MetaCAD benchmarks | Reproducible engineering validation cases and deterministic numerical calculations | Rust/WASM, validation, CI |
- Grounded RAG and agentic workflows with source provenance, contradiction gates, retries, validation, auditability, and explicit human review.
- Financial ML and trading research with microstructure features, leakage-resistant validation, realistic execution assumptions, and fail-closed risk controls.
- Native SwiftUI product backed by a 40-module Rust core, typed CoreFFI boundary, Core ML/ONNX delivery, and 223 core unit/integration tests.
- AVFoundation camera PPG pipeline with real-time DSP, quality gates, stabilization/hysteresis, and physical-device validation.
Private code, credentials, customer data, and proprietary screenshots are intentionally excluded. I can walk through architecture, trade-offs, failure modes, and selected sanitized code in a controlled interview.
- AI product and engineering roadmaps tied to measurable product, model, risk, and business outcomes
- LLM/RAG evaluation, grounding, guardrails, observability, and production readiness
- Python, PyTorch, scikit-learn, ONNX, APIs, data pipelines, SQL, CI/CD, and operational controls
- Core ML/ONNX, OCR/document intelligence, Swift/Rust boundaries, and private offline workflows
- Cross-functional delivery across ML, backend, mobile, product, design, QA, and release
Master’s candidate in Philosophy of Artificial Intelligence at Ufa University of Science and Technology (2026–2028). Focus areas include AI methodology, philosophy of mind, knowledge engineering, neural networks and machine learning, and the societal risks of intelligent systems.
Portfolio · AI/ML resume · LinkedIn · MetaCAD · Email