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Principal AI Engineer | GenAI, Edge AI, RAG & Agentic Workflows I build production-ready AI solutions, not just prototypes and demos. I am Matthew - a Principal AI Engineer and Data Scientist with over 20 years of experience solving complex enterprise technology and data problems. I specialize in Generative AI, Agentic workflows, machine learning, data engineering, and anticipating the next frontier of intelligent automation. Currently serving as a Principal AI Engineer at a Fortune 50 enterprise and holding an M.S. in Data Science from Northwestern University, I bring enterprise-grade architecture and rigor to businesses of all sizes. I don't just connect applications to an API; I understand the entire AI lifecycle. Furthermore, I architect future-proof systems—leveraging emerging paradigms like Edge AI, Small Language Models (SLMs), and Multi-Agent Swarms to ensure your tech stack is ready for the demands of 2027, 2030, and beyond. Here is how I can help you build something that actually works: 🔹 EDGE AI & NEXT-GEN ARCHITECTURE (2027+ Readiness) Edge AI & TinyML: Deploying lightweight, high-performance ML and AI models directly to IoT and edge devices for zero-latency, offline, and privacy-first capabilities. Small Language Models (SLMs) & Local AI: Fine-tuning and deploying highly efficient, domain-specific models that drastically cut cloud compute costs and keep enterprise data secure on-premise. Federated Learning: Architecting decentralized model training across distributed networks to maximize data privacy. Multi-Modal AI: Seamlessly integrating real-time vision, audio, and spatial data streams for advanced physical and ambient AI applications. 🔹 GENERATIVE AI & LLM APPLICATIONS Custom GenAI applications & Enterprise LLM/SLM solution architecture Prompt engineering, optimization, and structured outputs (tool calling) AI-powered document and intelligent workflow automation LLM evaluation, testing, guardrails, and optimization 🔹 AI AGENTS & AGENTIC WORKFLOWS Multi-agent swarms and complex autonomous AI orchestration LangGraph workflows & Tool-enabled agents Human-in-the-loop workflows & Model Context Protocol (MCP) Agent evaluation and enterprise production readiness 🔹 RAG & ENTERPRISE SEARCH Retrieval-Augmented Generation (RAG) architecture Embeddings, vector databases, and semantic search Knowledge-base assistants and document ingestion pipelines Retrieval accuracy and groundedness evaluation 🔹 MACHINE LEARNING & DATA ENGINEERING Predictive modeling, classification, segmentation, and anomaly detection Forecasting, time-series analysis, and recommendation systems Large-scale data processing (PySpark, Apache Spark, Databricks, Snowflake) MLOps architecture, continuous learning, and model validation 🏆 MY BACKGROUND & CREDENTIALS: Experience: 20+ years in enterprise tech, data, analytics, ML, and AI. Current Role: Principal AI Engineer / Data Scientist at a Fortune 50 enterprise. Education: M.S. in Data Science, Northwestern University. Innovation: U.S. Patent Inventor. Tech Stack: Azure, Databricks, Snowflake, Spark, Python, SQL, LangChain/LangGraph, Edge AI Frameworks, and modern AI/ML platforms. I am equally comfortable designing forward-looking AI architecture, building complex agentic workflows hands-on with Python, or translating deep technical concepts into clear business value for executives and stakeholders. Whether you need a cutting-edge Edge AI deployment, a robust RAG solution, or help taking an AI concept from a fragile idea to a secure, scalable production deployment, I am here to help. Have an AI, ML, or data challenge? Hit the "Invite" or "Hire" button, send me a message, and let's discuss what you’re building.

I build practical AI/ML systems for computer vision, industrial data, multimodal sensing, predictive modeling, and complex technical workflows. I hold a PhD in Electrical Engineering from Auburn University focused on applied AI and machine learning, with published research spanning multimodal anomaly detection, deep learning, computer vision, Industrial IoT, and AI-driven analysis of real-world data. My work is particularly well suited to projects where AI must interact with complex datasets, physical systems, images/video, sensor data, or specialized technical domains. I can help with: • Computer vision, image analysis, detection, segmentation, and tracking • Multimodal AI and sensor fusion • Anomaly detection and predictive modeling • Time-series and industrial sensor-data analysis • Deep learning model development and fine-tuning • LLM fine-tuning, RAG, agents, and retrieval systems • AI document intelligence and technical-data extraction • AI proof-of-concepts and feasibility studies • Python/PyTorch/OpenCV-based ML development • Model evaluation, optimization, and deployment • AI automation and API/software integrations Research & Technical Background My doctoral research focused on AI-driven anomaly detection using multimodal sensory data in Industrial IoT environments. This included developing methods for combining multiple data modalities and maintaining useful model performance when individual modalities are incomplete or unavailable. My published research also includes machine-learning image enhancement, deep-learning image classification, multimodal anomaly detection, and other applied AI topics. Beyond AI/ML, I have extensive professional engineering experience with industrial control systems, instrumentation, electrical design, and high-reliability industrial environments. This gives me a strong understanding of the physical and engineering context behind many AI applications—not just the software. How I Work I approach projects from an end-to-end engineering perspective: understand the underlying problem and data, establish a technically sound baseline, develop and evaluate the model, and turn the result into a working system. For early-stage or R&D projects, I can also rapidly develop a proof-of-concept to determine technical feasibility before committing resources to a larger implementation. I am comfortable working directly with founders, engineering teams, researchers, and technical stakeholders, and can take ownership of projects from initial technical scoping through implementation and validation. Top Rated Plus | 100% Job Success | PhD | Published AI/ML Research If you're working on a challenging AI/ML problem—particularly involving computer vision, multimodal data, industrial systems, predictive modeling, or specialized technical data—I'd be happy to discuss the project.

Columbia-educated Scientist (B.S. Astronomy) & Rutgers M.Ed. (Learning, Cognition, and Development) specializing in high-stakes learning architectures, Continuing Medical Education (CME), and EdTech R&D evaluation. In an economy flooded with generic, AI-generated content, I bridge the gap between cognitive psychology, data validation, and instructional design to engineer learning ecosystems that are pedagogically sound, legally compliant, and measurably effective. With 4,000+ billed hours and a 98% Job Success Score, I partner with enterprise organizations, clinical boards, and EdTech startups to deliver institutional-grade strategy and execution. Please skip to the section below that matches your current project needs: ================================================ 1. CONTINUING MEDICAL EDUCATION (CME) & CLINICAL ID ================================================ I translate highly complex medical manuscripts and clinical data into ACCME-compliant asynchronous learning paths, needs assessments, and interactive clinical case studies. • Proven Impact: Storyboarded major clinical modules for EDUmotion, CineMed, and iCareBetter. • Key Services: Medical e-learning curriculum development, grant proposal writing, professional practice gap analysis, and interactive patient-case branching scenarios. ========================================== 2. EDTECH R&D & SBIR/STTR FEASIBILITY STUDIES ========================================== I help venture-backed EdTech startups and small businesses validate their learning products, design pilot studies, and secure non-dilutive federal funding. • Proven Impact: Lead R&D consultant and literature review author for EdTech startup Wellbotics. • Key Services: Designing Phase IB usability/feasibility study protocols, logic models, usability surveys, IRB/human subject documentation, and qualitative data analysis plans. ========================================== 3. HIGH-STAKES PSYCHOMETRICS & TEST DESIGN ========================================== I design and audit cognitive measurement tools, pre-employment screening assessments, and academic test banks that withstand scientific and legal scrutiny. • Proven Impact: Engineered and audited auto-scored assessment tools for Indeed (used globally by hundreds of thousands of candidates) and served as Spatial Reasoning SME for eSkill Corporation. • Key Services: Multiple-choice question (MCQ) writing, item-writing guide creation, rubric development, and cognitive load audits. ============================================== 4. ENTERPRISE COMPLIANCE & MULTIMEDIA TRAINING ============================================== I design high-fidelity compliance training and technical courseware for rapid-growth compliance firms and enterprise clients. • Proven Impact: Evaluated and optimized Microsoft’s Courseware as Code environments within Azure DevOps for global partner upskilling. • Key Services: Harassment prevention, diversity/equity/inclusion (DEI), and unconscious bias training storyboarded for Articulate Storyline 360. Whether you need a Cognitive and Pedagogical Friction Audit of your existing course, a rigorous SBIR pilot study designed, or a complex clinical Needs Assessment written to win grant funding, I deliver scientific precision and educational expertise. Let’s build learning experiences that convert, retain, and educate. Message me or book a consultation below to get started.

I design and build production-grade security systems for companies that need to secure cloud infrastructure, pass audits, and operate in regulated environments. Most teams don’t have a security tool problem. They have an architecture, integration, and execution problem. That’s where I come in. What I Do I help startups and enterprise teams move from: ❌ fragmented tools and partial controls ❌ audit delays and failing security reviews ❌ reactive fixes and security debt → to ✅ engineered, scalable security architecture ✅ audit-ready, continuously compliant environments ✅ automated, integrated security operations Core Expertise Cloud Security & Cryptography • Multi-cloud security architecture (AWS, Azure, GCP) • TLS PKI systems with automated certificate lifecycle (IaC + CI/CD) • Encryption architecture (CMEK, KMS, data masking, data protection) • Cryptographic hardening aligned with FIPS and modern standards • DNS security, network isolation, and zero-trust patterns Identity & Access Management • SSO (SAML, OIDC), enterprise identity federation • RBAC and least-privilege system design at scale • SCIM provisioning and identity lifecycle automation • Integration with enterprise IdPs (PingFederate, Azure AD, Okta) • Cross-account and multi-environment access control Compliance & Audit Readiness • SOC 2, ISO 27001, PCI DSS, HIPAA, FedRAMP-aligned environments • End-to-end delivery: gap assessment → implementation → audit support • Control design, remediation, and evidence automation (Vanta, Drata, custom pipelines) • Continuous compliance monitoring vs point-in-time audits • Closing audit findings fast (not just identifying them) Security Engineering & Incident Response • CI/CD security (SAST, DAST, IaC scanning, secret management) • Vulnerability management with automated remediation workflows • Cloud misconfiguration detection (CIS benchmarks, runtime analysis) • Secure system design across infrastructure and application layers • Incident response, forensics support, and system hardening AI-Driven Security I don’t just integrate tools — I design security platforms. I have built and architected multi-agent AI-driven cybersecurity systems combining: • Cloud security analysis (AWS integrations, IAM analysis, misconfiguration detection) • Offensive security (recon, exploitation, privilege escalation simulation) • Vulnerability management (SAST, DAST, fuzzing, CI/CD integration) • SOC automation (SIEM/SOAR integrations, alert enrichment, playbooks) • Forensics and incident investigation workflows • Compliance reporting mapped to frameworks (SOC 2, ISO 27001, PCI, HIPAA) Key capabilities: • Multi-agent orchestration and communication • Automated remediation workflows • MITRE ATT&CK mapping and executive reporting • API-driven integrations across security tooling ecosystem • Role-based access for security, DevOps, and compliance teams This enables: → Continuous security instead of periodic assessments → 80% reduction in manual security effort → Faster audit readiness and real-time visibility How I Work • Engineering-first — I build and implement, not just advise • Work directly in production systems (cloud, identity, pipelines) • Design for real audit constraints, not theoretical compliance • Fast execution, clear communication, and ownership Typical Clients • SaaS companies preparing for SOC 2 / ISO 27001 / HIPAA • Cloud-native platforms handling sensitive or regulated data • Startups entering enterprise sales with security blockers • Organizations with fragmented security tools that don’t work together Important I’m not a fit for checklist-based security or surface-level audits. If you need: • real security architecture • working implementations • systems that pass audits and hold up in production - we’ll work well together.

𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝: PhD in Computer Science; ex-Director of ML Engineering & Research at Nuro and Gatik; ex-Chief Scientist at Huawei Research; 45 publications, 40 patents. I've led 25-person AI teams; the part I never delegated is what you get here: the architecture and the code. Full-time, hands-on. I build ML systems that make it to production. If your agents loop and burn tokens, your fine-tune won't converge, or accuracy has plateaued and nobody knows why - that's the work I do best. 20 years from DSP firmware to frontier multimodal AI: I debug from silicon up, not by guessing. 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐝 ➤ AI Agents: systems that plan, call your tools through custom MCP servers, and check their own output - LangGraph agents that finish the job at predictable cost. ➤ LLM Applications: your documents turned into chatbots and assistants that answer with citations and refuse to guess - RAG with hybrid retrieval, accuracy measured, never assumed. ➤ LLM Fine-Tuning: open models adapted to your task, distilled and quantized - frontier quality at a serving cost you can afford. ➤ Computer Vision: face recognition shipped to millions of Huawei devices; gigapixel medical imaging; detection, video search, and image enhancement that fit real latency budgets, cloud or edge. ➤ Traditional ML: credit scoring, fraud and anomaly detection, churn prediction, recommenders, forecasting - gradient boosting to deep nets, calibrated and shipped to production - my day job as VP of R&D at a FinTech holding. ➤ Data Science & Analysis: exploratory data analysis, A/B testing, uplift and cohort analysis - numbers your team can act on. ➤ SaaS Integration: AI embedded into your existing product - backend services, APIs, webhooks - features that ship inside your SaaS, not beside it. ➤ MLOps & Backend: the pipelines, serving, and CI/CD around your model - systems depth down to firmware (30+ patents at LSI/Broadcom). Everything ships hardened: I authored AdvHat, the real-world FaceID attack - I know how models fail before your users do. 𝐑𝐞𝐜𝐞𝐧𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 🏅 FinTech - agentic underwriting: LangGraph agents with custom MCP servers read bank statements and tax forms (OCR + VLM, field-level confidence); no decision ships without rule-engine sign-off. Review time down ~70%, fraud recall up 18 points with false positives flat, token cost capped - gated by a golden-set suite in CI. 🏅 Healthcare - gigapixel pathology: billion-pixel slides, a multi-scale transformer over tile embeddings, and an LLM that drafts findings with every claim linked to slide coordinates - traceability that won clinical trust. Case review time down ~40%; inference cost cut 5x without losing rare-finding sensitivity. 🏅 Autonomous driving: at Nuro, diffusion planning + RL motion selection (CIMRL) and scenario generation (DiffScene, AAAI 2025), gated by closed-loop simulation; at Gatik, online mapping that cut HD-map staleness. SafeAuto (ICML 2025) leads driving QA benchmarks. 𝐖𝐡𝐞𝐫𝐞 𝐈'𝐯𝐞 𝐖𝐨𝐫𝐤𝐞𝐝 🌐 VP of Engineering / Head of R&D at a FinTech holding; Adjunct Professor at Sofia University (Palo Alto) 🌐 Head of AI at Logical Intelligence 🌐 Sr. Director of AI Engineering & Research at Gatik (Silicon Valley) 🌐 Director of ML Engineering & Research at Nuro (Silicon Valley) 🌐 Chief Scientist at Huawei Research 🌐 Managing Director at the Artificial Intelligence Research Institute 🌐 Senior Software Engineer at LSI (now Broadcom) - 6 years fully remote for a US company Remote, hybrid, or embedded - I've delivered in all three. 𝐒𝐭𝐚𝐜𝐤 • GenAI/LLM: OpenAI, Anthropic, Gemini; LangGraph, LangChain, LlamaIndex, MCP, RAG, NLP, Hugging Face Transformers, vLLM, LoRA/QLoRA/PEFT, quantization, distillation, guardrails • ML/DL: PyTorch, JAX, scikit-learn, XGBoost, LightGBM, diffusion models, RL, OpenCV, CUDA, C++ • Data science: pandas, NumPy, A/B testing, uplift, uncertainty quantification, forecasting, SHAP • Data engineering: SQL (PostgreSQL, MySQL, Oracle PL/SQL), Kafka, Airflow, Ray, feature stores, petabyte-scale pipelines • Vector search: FAISS, Pinecone, Weaviate, pgvector, Elasticsearch • Backend: Python, FastAPI, REST/gRPC microservices, Postgres, Redis • MLOps/Cloud: AWS (SageMaker), GCP (Vertex AI), Docker, Kubernetes, CI/CD, Triton, ONNX/TensorRT, MLflow, W&B 📌 I've usually already solved the harder version of your problem. Every engagement ships with an eval you can rerun; your team keeps the knowledge - I founded Nuro ML University (100+ engineers). Graded on delivery, not just papers: a dozen corporate awards at Huawei. Plain-English updates, async-friendly, US/EU hours. Not ready for a full build? Start with a fixed-scope audit - architecture review, eval coverage, and a prioritized fix list you can execute with or without me. Send me the problem - what you're building, what's failing, what "good" looks like - and I'll reply with a concrete assessment and scoped plan within one business day. Alex

Technical Product Lead | LLM Systems & ML Infrastructure | MLOps AI product tech lead with a background in applied machine learning and systems architecture. Specializes in the productization of large language models (LLMs), recommendation systems, and computer vision pipelines. Fluent in the trade-offs between model fidelity, inference latency, compute cost, and statistical accuracy. Experienced in translating experimental research papers and half-trained checkpoints into production-grade, distributed systems. Operates at the intersection of prompt engineering, data ontology, and backend infrastructure. ###Technical Strategy & Systems Thinking - LLM Architecture Strategy: Roadmapping across fine-tuned OSS models (Llama 3, Mistral), closed-weight APIs (OpenAI, Anthropic), and hybrid routing layers. Defining context window utilization, retrieval-augmented generation (RAG) chunking strategies, and embedding model selection (e.g., Ada vs. Cohere vs. SBERT). - ML Evaluation & Validation: Designing offline/online evaluation frameworks beyond accuracy—specializing in hallucination rate, perplexity, toxicity filters, and adversarial robustness. Experience with human-in-the-loop (HITL) labeling workflows and active learning loops. - Infrastructure & MLOps: Defining requirements for feature stores, model registries, and inference orchestration. Deep understanding of GPU/TPU utilization, autoscaling policies, and cold-start mitigation. Familiar with Kubernetes, Ray, and vector database sharding strategies. - Data-Centric AI: Prioritizing data curation over architecture tweaks. Expertise in synthetic data generation, class imbalance correction, and weak supervision (Snorkel/Skweak) for low-resource domains. ###Technical Stack & Implementation Fluency - Languages & Querying: Python (scripting, data analysis), SQL (complex aggregations, feature engineering), GraphQL/REST. - Frameworks & Libraries: LangChain, LlamaIndex, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn, spaCy. - Infrastructure & Tooling: AWS SageMaker, Bedrock; GCP Vertex AI; Databricks; Weights & Biases; MLflow; Docker; Kubernetes. - Vector/NoSQL: Pinecone, Milvus, Chroma, Redis, PostgreSQL (pgvector). - Experimentation: A/B testing with inference shadows, canary deployments, multi-armed bandit algorithms. ###Technical Implementation Highlights 1. RAG Architecture for Enterprise Q&A *Led product requirements for a retrieval-augmented generation system targeting legal document analysis. Evaluated trade-offs between dense (DPR, ColBERT) and sparse (BM25) retrievers. Defined chunking ontology and metadata filtering schemas to reduce latency from 2.3s to 480ms while maintaining top-3 recall above 89%. Implemented re-ranking layer to improve answer relevancy by 32%.* 2. LLM Fine-Tuning & Cost Optimization *Managed roadmap for domain-adaptation fine-tuning of a 7B parameter model. Instrumented LoRA vs. full fine-tuning experiments to optimize for VRAM constraints. Reduced inference cost per 1M tokens by 63% through quantization (bitsandbytes) and speculative decoding implementation. Collaborated with MLEs to build a feedback loop using human preference data for DPO training.* 3. Real-Time Computer Vision Pipeline *Shipped an on-device object detection model for mobile IoT. Defined requirements for model compression (TensorFlow Lite, pruning) to meet <15MB size constraint and 30 FPS threshold on edge devices. Implemented frame-sampling strategy to reduce cloud egress costs by 73%.* 4. Model Observability & Drift Detection Implemented statistical alerting for model performance degradation in production. Defined thresholds using PSI (Population Stability Index) and KL divergence on embedding distributions. Built requirements for explainability layer using SHAP and LIME to debug failure modes flagged by trust & safety teams.