Sales Prompt & Logic Audit
Audit existing prompts to identify hallucination triggers and logic gaps.
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2 Days
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I will curate & clean large-scale datasets for AI/llm training
Sales Prompt & Logic Audit
Audit existing prompts to identify hallucination triggers and logic gaps.
Delivery Time
2 Days
Package Includes: see all
Basic
...
Sales Prompt & Logic Audit
Audit existing prompts to identify hallucination triggers and logic gaps.
Delivery Time
2 Days
Package Includes: see all
Standard
...
Premium
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Service details
Quality data is the absolute bottleneck of any modern machine learning operation. You cannot achieve state-of-the-art model intelligence if your pre-training corpus is filled with anomalous, unstructured, or inherently biased data. I specialize in the heavy lifting of machine learning: architecting high-throughput data curation and cleaning pipelines designed specifically for LLM pre-training and fine-tuning environments.
I build custom, asynchronous ingestion pipelines that can process massive datasets without stalling your remote infrastructure. Moving well beyond basic regex scripts, I implement strict, deterministic validation models using Python and Pydantic. This ensures every single record in your dataset matches your exact target schema before it ever reaches your distributed GPU clusters.
My Core Engineering Focus Includes:
- High-Volume Data Ingestion: Building robust backend pipelines to scrape, aggregate, and normalize unstructured text from highly diverse sources.
- Deterministic Validation: Enforcing rigorous JSON schema constraints so your model ingests perfectly formatted, high-quality tokens.
- Anomaly Detection & Filtering: Programmatically identifying and stripping out duplications, low-quality text, and malformed records.
- Compliance & Masking: Executing secure pipelines that strip Personally Identifiable Information (PII) to ensure absolute GDPR compliance.
Your architecture team should be focused on tuning distributed environments and optimizing context windows, not fighting with messy data loaders. I will deliver the clean, structured, and highly optimized datasets your models require to scale intelligently. Send me a sample of your raw data and your target schema, and let's get your training pipeline fed correctly.
Key details
- Service TypeModel TrainingFeature EngineeringModel Deployment
- Platform / FrameworkTensorflowScikit-LearnPytorchXgboost
- Tech Stack / ToolsTensorflowExcelDockerRstudio
- Programming LanguagePythonJavaMatlabSQL
- APISMicrosoft Computer Vision AIAmazon RekognitionGoogle Cloud Vision APIAzure Face APIIBM Watson Visual Recognition
Special note from freelancer
I provide deterministic cleaning at scale, ensuring your model training runs are never stalled by malformed or low-quality data payloads.
Comparison
Custom B2B Sales Agent
Engineer a strict prompt framework and deploy a functional, grounded AI agent.
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5 Days
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Full CRM & Data Integration
Standard tier plus automated CRM routing and real-time database context retrieval.
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14 Days
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FAQs
- I deliver token-optimized JSONL, Parquet, or SQL files, fully structured for immediate ingestion into your training environment.
Khaal
About Khaal
Machine Learning Engineer |Database Administration |Backend Developer
Stop paying for scripts that break in production. I am a Senior Backend and ML Engineer specializing in robust data infrastructure and deterministic AI workflows. I build edge-case-proof architectures that scale securely. Core Expertise: Unstructured Data to JSON Pipelines LLM Evaluation and Validation High-Concurrency PostgreSQL Architecture Secure Python API Automation I do not use no-code tools. Let's build an enterprise architecture that actually works.
Stop paying for scripts that break in production. I am a Senior Backend and ML Engineer specializing in robust data infrastructure and deterministic AI workflows. I build edge-case-proof architectures that scale securely. Core Expertise: Unstructured Data to JSON Pipelines LLM Evaluation and Validation High-Concurrency PostgreSQL Architecture Secure Python API Automation I do not use no-code tools. Let's build an enterprise architecture that actually works.
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