I am an AI Engineer at Seagate Technology focused on practical GenAI automation, internal engineering workflow tooling, and backend/data infrastructure. My current work includes multi-agent AI workflows for requirements analysis, debugging, testing, and PR preparation, plus C# internal software, VictoriaMetrics TSDB setup, and Kafka-to-TSDB ingestion validation with Apache Flink/Java.
Previously, I built full-stack applications and internal automation tools with React, Next.js, FastAPI, PostgreSQL, Directus, Supabase, and Docker. I focus on turning AI prototypes into usable software: reliable APIs, clean data flows, practical UX, and deployment-ready architecture.
| Priority | Target roles | Evidence in this profile |
|---|---|---|
| 1 | AI Engineer | RAG apps, LLM agents, multimodal extraction, AI automation, evaluation-minded delivery |
| 2 | GenAI Engineer / Data Engineer | LangChain, OpenAI, Gemini, pgvector, PostgreSQL, Kafka/Flink validation, VictoriaMetrics |
| 3 | Data Science / Data Analyst / Full-Stack / Frontend | ML projects, analytics dashboards, React/Next.js, FastAPI, SQL, dashboards, API integrations |
| Area | Tools and Strengths |
|---|---|
| AI / ML | Python, TensorFlow, PyTorch, scikit-learn, LLMs, RAG, LangChain, OpenAI, Gemini, Ollama, prompt engineering |
| Data / Infrastructure | Apache Kafka, Apache Flink, VictoriaMetrics, PostgreSQL, pgvector, Redis, SQL, time-series data, ingestion validation |
| Software Engineering | TypeScript, Next.js, React, Node.js, FastAPI, C#, Java, Docker, CI/CD, Git/GitLab, REST APIs |
| Applied AI | Agentic workflows, computer vision, OCR/entity extraction, multilingual summarization, document retrieval, structured AI outputs |
- AIAT Super AI Engineer Season 6: Foundation AI (Theory) - Artificial Intelligence Association of Thailand, 2026
- Anthropic Academy certificates across Claude API, Claude Code, Model Context Protocol, subagents, agent skills, and AI Fluency
- Google Cloud AI/ML skill badges across Vertex AI, Gemini, Imagen, Multimodal RAG, BigQuery ML, Document AI, and ML APIs
- AIS Academy Prompt Engineering & Agentic AI
| Project | Why it matters | Stack |
|---|---|---|
| Wellness AI Assistant | Production-style RAG chatbot with document ingestion, vector search, authenticated chat, streaming responses, and tool-calling patterns | Next.js, TypeScript, LangChain, Supabase, PostgreSQL, pgvector, OpenAI |
| Receipt AI Expense Tracker | Multimodal receipt parser that extracts structured JSON from Thai/English receipts and visualizes spending analytics | Next.js, Gemini Vision, Supabase, Recharts |
| AI Resume Matcher | Resume/JD matching tool with PDF parsing, structured extraction, skill-gap analysis, career guidance, and interview prep | React, Python, FastAPI, Gemini, Vercel |
| AI Product Listing Assistant | Image-to-product-listing generator with multilingual output, FastAPI backend, retry/circuit-breaker patterns, and test coverage | Python, FastAPI, Streamlit, Gemini Vision, pytest |
| Customer Support AI System | Multimodal support-ticket analysis for category, sentiment, priority, and response drafting | React, FastAPI, Gemini, Playwright, pytest |
- Building GenAI workflow automation for requirements analysis, debugging, testing, and PR preparation.
- Shipping RAG, multimodal AI, and agentic applications with production-minded APIs and data flows.
- Strengthening backend/data infrastructure with time-series storage, Kafka/Flink validation, and PostgreSQL/pgvector.
- Hardening AI applications with better evaluation, observability, testing, and deployment workflows.
- Email: pakon.poomson@gmail.com
- LinkedIn: linkedin.com/in/pakon-poomson
- Portfolio: portfolio-pakon.netlify.app