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Felix Peng
AI Solutions Engineer | LLM Applications | Enterprise AI
I work at the intersection of applied AI and enterprise delivery—turning LLM capabilities into practical systems through RAG, post-training, evaluation, agents, and solution architecture.
01 / About
Practical AI, built for real operating environments.
My work focuses on the path between a promising model and a dependable solution: framing the use case, designing the system, evaluating behavior, and helping teams adopt it.
I am building this portfolio to document applied work across LLM post-training, retrieval systems, agentic workflows, and enterprise AI delivery. Verified background details will be added as the profile is completed.
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AI Solutions EngineerSolutions ArchitectTechnical Account ManagerCustomer Success EngineerDeployment Strategist
02 / Experience
Enterprise-facing technical work.
Previous experience
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03 / Featured projects
Case studies in applied AI systems.
Each project follows the same evidence-first structure, with clear placeholders where verified details are not yet available.
Tax LLM SFT & Evaluation
A structured case study for supervised fine-tuning and comparing a base model with its fine-tuned counterpart in a tax-domain setting.
Tax RAG Evaluation
A retrieval evaluation case study comparing lexical, sparse, dense, and fused retrieval approaches for tax-domain knowledge.
CSGClaw Multi-Agent Evaluation
An enterprise AI evaluation case study centered on a coordinated Research, Writer, and Reviewer agent workflow.
AI Search Copilot
A search-assisted LLM application case study combining external search, retrieval-augmented generation, and evaluation.
04 / Skills
Capabilities across the AI solution lifecycle.
Applied AI
- LLM applications
- RAG
- SFT / QLoRA
- Evaluation
- AI agents
Retrieval
- TF-IDF
- BM25
- Dense retrieval
- RRF
- Search APIs
Solution Delivery
- Solution architecture
- Technical discovery
- Enterprise deployment
- Customer enablement
Engineering
- Python
- TypeScript
- API integration
- Experiment design
- Technical documentation
05 / Resume
Full experience, in one document.
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06 / Contact
Let's talk about applied AI.
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