Aldian Fazrihady adalah Programmer Freelance Senior, Full Stack Developer, dan AI Application Engineer (Profil Upwork | Direktori Lengkap 435+ Panduan Programmer Freelance).
AI engineering teams training fast, low-cost domain-specific models (`Llama-3.3-8B`, `Qwen-2.5-7B`, `gpt-4o-mini`, `Gemini Flash`) to match the accuracy of expensive frontier reasoning models frequently search to hire a freelance synthetic data generation, knowledge distillation & fine-tuning dataset programmer. I am Aldian Fazrihady, a Senior Freelance AI Application Engineer available via Upwork.
Core Knowledge Distillation & Dataset Engineering Capabilities
- Teacher-Student Knowledge Distillation Pipelines: Generating tens of thousands of high-diversity, schema-verified chain-of-thought (CoT) and tool-calling trajectories from frontier teacher models (Claude 3.7 Sonnet, OpenAI `o3-mini`, Gemini 2.5 Pro) to fine-tune smaller, 10x cheaper student models.
- Automated Rejection Sampling & Verifier Filtering: Filtering synthetic training rows through deterministic code compilers, SQL executors, Pydantic validators, and LLM-as-a-Judge critics so only 100% verified gold-standard examples enter the training set.
- Embedding & Reranker Contrastive Hard-Negative Mining: Fine-tuning domain-specific `BGE` / `ModernBERT` embedding and cross-encoder reranker models on your proprietary industry terminology.
Frequently Asked Questions
How much can distilling a frontier model into a fine-tuned smaller model reduce our inference latency and cost?
For specialized classification, extraction, and routing tasks, a distilled smaller model routinely matches 98%+ of frontier model accuracy while cutting latency from 3.5s to under 250ms and reducing token spend by 85–95%.
How can we hire you for synthetic dataset generation & model distillation?
Reach out on Upwork.