Rashid W.

Key Skills

Software

AWS SageMaker

Top Subject Matter

Large Language Model (LLM) fine-tuning and MLOps for legal/compliance documents

De-identification of PHI from Electronic Health Records (EHR)

Legal Services & Contract Review

Top Data Types

Document

Text

Top Task Types

Fine-tuning

Prompt + Response Writing (SFT)

Freelancer Overview

AI Enterprise Startup — Distributed LLM Infrastructure (Freelance Technical Consultant). Brings 9+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include AWS SageMaker, Internal, and Proprietary Tooling. Education includes Doctor of Philosophy, Columbia University (2018) and Master of Science, New York University (NYU) (2014). AI-training focus includes data types such as Computer Code, Programming, and Document and labeling workflows including Fine-tuning and Prompt + Response Writing (SFT).

Labeling Experience

HealthTech Client — Enterprise HIPAA Data Lake (Freelance Technical Consultant)

DocumentPrompt + Response Writing (SFT)

Designed and implemented a zero-trust serverless data lake and automated processing pipelines to handle sensitive healthcare records. Parsed, sanitized, and masked protected health information (PHI) from live EHR feeds to produce de-identified datasets suitable for downstream analytics. Ensured compliance outcomes through security controls and validation suitable for regulated data environments. • Designed a zero-trust serverless data lake on AWS using Glue, Athena, and encrypted S3 storage buckets. • Engineered automated Python pipelines to parse, sanitize, and mask PHI from live EHR feeds. • Implemented encrypted storage and secure processing patterns for regulated health data. • Passed a third-party HIPAA and SOC 2 Type II compliance audit with zero discrepancies.

2019 - 2021

AI Enterprise Startup — Distributed LLM Infrastructure (Freelance Technical Consultant)

AWS SageMakerFine-tuning

As an independent technical consultant, built and operationalized distributed LLM training workflows for fine-tuning open-source large language models on proprietary legal and compliance documents. Orchestrated multi-node GPU training with scalable infrastructure and managed the end-to-end MLOps lifecycle for rapid iteration and evaluation cycles. The work focused on transforming document corpora into model-ready datasets and production-grade training pipelines for downstream inference use cases. • Assembled an end-to-end MLOps pipeline for fine-tuning open-source LLMs. • Used Kubernetes and Ray with AWS SageMaker to orchestrate distributed model training across multi-node GPU clusters. • Reduced model evaluation cycles from 5 days to less than 12 hours through pipeline and training optimization. • Delivered infrastructure changes to support high-volume proprietary legal/compliance document domains.

2019 - 2021

Education

Work History