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You will get an AI backend reliability diagnosis and production-ready fix

Yu-Ting L.Status: Offline
Yu-Ting L.
Preview image

Let a pro handle the details

Buy Generative AI services from Yu-Ting, priced and ready to go.
Yu-Ting L.Status: Offline
Yu-Ting L.
Preview image

Let a pro handle the details

Buy Generative AI services from Yu-Ting, priced and ready to go.

Project details

Stop guessing why your FastAPI or LLM backend fails in production. I will reproduce one scoped reliability issue, isolate its root cause, and deliver a documented fix with focused tests (Standard/Advanced). Typical cases include async or concurrency bugs, provider timeouts, retry storms, malformed structured outputs, streaming failures, authentication or quota edge cases, and missing observability. Advanced adds hardening for validation, retries, error handling, and monitoring. You receive reproducible findings, code for the agreed scope, tests, and clear operational trade-offs. I work async-first with written updates. Suitable for Python/FastAPI APIs using PostgreSQL or Supabase, Redis, Docker, current model providers, and custom agentic workflows.
AI Algorithms
Large Language Model, Transformer Model
AI Applications
AIOps, Anomaly Detection, Natural Language Understanding
AI Development Language
Python
AI Tools
Hugging Face
What's included
Service Tiers Starter
$200
Standard
$450
Advanced
$800
Delivery Time 3 days 5 days 7 days
Number of Revisions
122
AI Model Integration
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Batch Normalization
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Database Integration
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Detailed Code Comments
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Image Upscaling
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MLOps
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Model Deployment
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Model Documentation
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Model Monitoring
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Model Testing & Optimization
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Model Tuning
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Natural Language Processing
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NLP Tokenization
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Pre-Training
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Prompt Engineering
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Setup File
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Source Code
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Frequently asked questions

Yu-Ting L.Status: Offline

About Yu-Ting

Yu-Ting L.Status: Offline
AI Systems Engineer | Complex Data, Retrieval & Agentic Workflows
Taipei, Taiwan - 11:42 am local time
I build production AI systems that transform complex, heterogeneous data into reliable structured information and precise, evidence-grounded retrieval. My work covers data ingestion and transformation, hybrid retrieval and reranking, retrieval evaluation, and agentic workflows with tool use, validation, observability, and human review.

I also build the platform around these systems: PostgreSQL data models, APIs, background jobs, access control, usage metering, billing and payment flows, and failure recovery. Technologies such as FastAPI, Supabase, Stripe, OCR, and VLMs are implementation choices selected according to the project—not my professional positioning.

I have worked in AI engineering and research since 2021. Since January 2024, I have led product engineering and research at Apertis AI, where I build:

• Verbatim: multimodal ingestion for PDF, Office documents, and OCR; hybrid Qdrant and lexical retrieval; reranking; source citations; and evaluation.
• Production AI infrastructure: multi-model routing, provider failover, usage metering, cost controls, Stripe billing, authentication/RLS, and APIs.
• Reliable agent workflows: tool calling, structured outputs, validation, retries, observability, and human review.
• Open-source AI: merged contributions to LlamaIndex, Vercel AI SDK, Kilo Code, Lightning AI’s lit-llama, Lobe Icons, and CAG across Python and TypeScript ecosystems.

Core stack:
Python, FastAPI, Go, PostgreSQL, Supabase, Qdrant, BM25S, Redis, React, TypeScript, Next.js, Docker, and OpenAI/Anthropic APIs.

Best-fit projects:
• RAG quality, retrieval, citation, or evaluation improvements
• Document parsing, OCR, and multimodal ingestion
• AI agents, tool integrations, and backend reliability
• Complete AI SaaS features from API to UI
• LLM routing, metering, billing, and observability

Research credentials:
I am the first author of a CIKM 2022 paper and a recent preprint on retrieval bottlenecks in multi-hop document question answering. I have served on the LREC 2026 Scientific Committee and as a program committee member or reviewer for ROCLING, LREC-COLING, and COLING.

I work async-first, communicate through clear written updates, and provide reproducible deliverables. I am available up to 50 hours per week. Scheduled calls are possible between 9:00 PM and 12:00 AM Taiwan time (UTC+8).

Steps for completing your project

After purchasing the project, send requirements so Yu-Ting can start the project.

Delivery time starts when Yu-Ting receives requirements from you.

Yu-Ting works on your project following the steps below.

Revisions may occur after the delivery date.

Reproduce and isolate the failure

I reproduce the issue from your minimal example, logs, traces, or staging environment and document the root cause.

Implement the scoped fix

I implement the agreed correction with a small, reviewable change that fits the existing architecture.

Review the work, release payment, and leave feedback to Yu-Ting.