Hao Hoang

Founder

United States4 yrs 4 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Built scalable AI systems for diverse applications.
Grew a community of 50k+ AI engineers.
Expert in LLMs and AI interview preparation.

Stackforce AI infers this person is a highly skilled AI researcher and engineer specializing in LLMs and machine learning applications.

Contact

Skills

Core Skills

Machine LearningLarge Language Models (llm)Artificial Intelligence (ai)Recommendation SystemsData ScienceAnomaly Detection

Other Skills

Technical WritingCommunity BuildingResearch SkillsAgentic RAGRAGRetrieval-Augmented Generation (RAG)KotlinPythonComputer VisionBack-End Web DevelopmentDeep LearningNatural Language Processing (NLP)MLOpsGenerative AIAgile Project Management

About

Daily insights on LLM systems, AI engineering, agentic workflows, and real-world machine learning. I write about: - AI interview questions (LLM engineering, system design, research) - LLM infrastructure & optimization - RAG architectures (GraphRAG, Agentic RAG, Hybrid) - Vision-Language Models (VLMs) - AI research papers explained simply - Building and deploying AI products at scale My work focuses on the intersection of research and engineering, turning cutting-edge papers into production-ready AI systems. I help: ✔ AI engineers understand complex concepts ✔ Teams build scalable, reliable LLM systems ✔ Students and developers break into AI ✔ Companies turn research into real products If you want to level up in AI one day at a time, you’re in the right place. 📬 AI Interview Prep Newsletter: https://aiinterviewprep.substack.com/ ☕ Support my work: https://buymeacoffee.com/haohoang

Experience

4 yrs 4 mos

Total Experience

1 yr 3 mos

Average Tenure

2 yrs 7 mos

Current Experience

Ai interview prep

Author & Founder - AI Interview Prep

Oct 2025 – Present · 7 mos · Los Angeles, California, United States · Remote

Helping 50,000+ AI engineers and researchers master the "hidden" technical traps of top-tier AI interviews (OpenAI, Anthropic, DeepMind).
As the author of AI Interview Prep, I break down complex LLM system design, reinforcement learning, and vision-language models into high-signal, actionable insights.
Key Achievements & Responsibilities:
Content Creation: Author of the "Daily AI Interview Questions" series, focusing on advanced concepts like GraphRAG, RLHF collapses, and VLM spatial grounding.
Community Growth: Scaled a high-intent audience to 50k+ followers on LinkedIn and 10k+ active subscribers on Substack.
Industry Impact: Provided career-critical preparation for senior-level candidates transitioning into elite AI research and engineering roles.
🔗 Read more and subscribe: aiinterviewprep.substack.com

Machine LearningLarge Language Models (LLM)Technical WritingCommunity Building

Layerproof

AI Researcher

Sep 2025 – Present · 8 mos · Los Angeles, California, United States · Remote

🔹 AI Systems & Chatbots
Researched and integrated state-of-the-art LLMs, RAG, and agentic AI patterns into productivity chatbots for founders.
Designed knowledge retrieval pipelines and multi-step reasoning workflows for investor-focused insights.
Consulted on AI architecture, SoTA model selection, prompt engineering, and LLM system design.

Artificial Intelligence (AI)Research SkillsAgentic RAGRAGLarge Language Models (LLM)

Spartan

AI Researcher and Software Engineer

Oct 2023 – Present · 2 yrs 7 mos · Los Angeles, California, United States · Remote

🔹 Generative AI & LLM Systems
Built and deployed agentic AI pipelines combining LLMs, RAG (GraphRAG, LightRAG, PathRAG), and vector search for real-world applications.
Designed prompt engineering patterns for multi-step reasoning, classification, summarization, and code review automation.
Fine-tuned and benchmarked LLMs for domain-specific tasks (coding, PDF parsing, technical diagram understanding).
🔹 AI-Powered Product Prototypes
J.D. Power / Template Builder: Designed modular AI pipelines for parsing automotive incentive PDFs using OCR, rule-based methods, LLMs, and VLMs. Reduced template creation time significantly.
Approvia / Code Review AI: Developed agentic LLM workflows for automated code review; integrated fine-tuned models for code-specific reasoning.
Datum / Technical Diagram Understanding: Applied VLMs + LLMs to interpret, segment, and extract structured data from complex engineering drawings.
Pravah / Energy Forecasting: Built ML/DL pipelines for time-series demand forecasting, including ETL pipelines, retraining workflows, and motif/discord detection via Matrix Profile.
Novoflow / AI Appointment Agent: Researched and integrated grounding models, agentic LLM workflows, and VLMs for automated user interactions.
🔹 Knowledge Sharing & Leadership
Daily sharing of AI research, system design insights, and interview Q&A with 30k+ LinkedIn followers and Substack readers.
Led AI knowledge-sharing sessions internally; advised on team AI strategy and candidate hiring.

Artificial Intelligence (AI)Large Language Models (LLM)Retrieval-Augmented Generation (RAG)KotlinPythonComputer Vision+2

J.d. power

Senior AI Engineer

Oct 2023 – Mar 2025 · 1 yr 5 mos · Los Angeles, California, United States · Remote

🔹 LLM Systems & RAG
Designed end-to-end RAG pipelines using LangChain and LlamaIndex.
Built structured extraction systems for thousands of complex automotive incentive PDFs.
🔹 AI Infrastructure
Built and operated full backend + ML infra on GCP, Kubernetes, Terraform, Helm.
Designed high-reliability systems for large-scale batch inference & parsing.
🔹 PDF Intelligence Research
Integrated Azure Document Intelligence with custom OCR + NLP pipelines.
Improved accuracy across highly variable OEM document formats (Toyota, Ford, Hyundai, VW…).
🔹 Team & Client Collaboration
Led sprint planning, architecture decisions, and client communication.

Large Language Models (LLM)Natural Language Processing (NLP)Artificial Intelligence (AI)MLOpsGenerative AIDeep Learning+1

Viettel big data analytics center

Data Scientist

Jan 2023 – Dec 2023 · 11 mos · Hanoi Capital Region · On-site

🔹 Recommendation Systems & AI Personalization
Designed AI-powered recommendation systems using collaborative filtering, content-based methods, and deep learning (REC-VAE).
Built scalable ETL pipelines with PySpark and Pentaho for large-scale data processing.
Deployed personalization dashboards for TV360 content recommendation using AI pipelines.
🔹 AI Research & Experimentation
Experimented with hybrid recommendation models combining embeddings + LLM reasoning.
Implemented metrics-based optimization for user engagement and retention.

Recommendation SystemsExtract, Transform, Load (ETL)Big DataDeep LearningData Science

Kratos defense and security solutions

Data Scientist

Jan 2022 – Jan 2023 · 1 yr · Labège, Occitanie, France · Hybrid

🔹 Time Series & Anomaly Detection
Developed AI pipelines for anomaly detection on telemetry and sensor data using Matrix Profile.
Designed predictive maintenance systems leveraging deep learning and time-series motif discovery.
Applied AI reasoning pipelines to summarize and explain anomalies in real-time dashboards.
🔹 AI Integration & Automation
Experimented with LLM summarization for technical report generation.
Designed hybrid AI workflows combining rules + ML predictions for critical system monitoring.

Time Series AnalysisAnomaly DetectionData Science

Toulouse mathematics institute

Research Assistant In Optimization

Nov 2018 – Sep 2021 · 2 yrs 10 mos · Toulouse, Occitanie, France · On-site

🔹 AI & Optimization Research
Studied convergence of inertial dynamics (Nesterov accelerated gradient) and applied Lyapunov energy methods.
Designed AI-driven optimization experiments for accelerated convergence in large-scale systems.
Developed simulation frameworks to validate optimization algorithms on high-dimensional data.
🔹 Applied AI & Research Mentorship
Integrated mathematical optimization methods with AI model training pipelines for efficiency gains.
Mentored students and collaborators on AI applications of numerical optimization and algorithm design.
Published research on Hessian-driven damping and fast convergence of inertial dynamics.