For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

Invite to Job
M
Mohsin

Mohsin

Senior Software Engineer - RLHF Feedback Loop for AI Training (Ramp)

USA flagRemote, Usa
Expert

Key Skills

Software

No software listed

Top Subject Matter

Autonomous Finance
AI Vendor Negotiation
Contract Automation

Top Data Types

TextText
DocumentDocument

Top Task Types

RLHFRLHF
ClassificationClassification
Entity (NER) ClassificationEntity (NER) Classification

Freelancer Overview

Senior Software Engineer - RLHF Feedback Loop for AI Training (Ramp). Brings 12+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Master of Science, Stratford University (2015) and Bachelor of Science, University of Toronto (2007). AI-training focus includes data types such as Text and Document and labeling workflows including RLHF, Classification, and Entity (NER) Classification.

Expert

Labeling Experience

Senior Software Engineer - RLHF Feedback Loop for AI Training (Ramp)

TextRLHF
As a Senior Software Engineer at Ramp, I developed RLHF (Reinforcement Learning from Human Feedback) feedback loop systems to enhance the learning of AI-driven vendor negotiation agents. My work focused on integrating AI feedback into orchestration workflows, leveraging LLMs and custom guardrails for financial contract automation. Significant efforts involved orchestrating AI training data collection through asynchronous human-in-the-loop evaluations and LLM assessment tools. • Designed and implemented Ruby/Node.js AI communication clients for collecting annotated human feedback on LLM-generated contract recommendations. • Integrated Go safety guardrails and regex interceptors to ensure quality and safety of training data. • Automated data collection pipelines enabling AI retraining iterations using RLHF methodologies. • Leveraged internal proprietary AI tooling and Python orchestration services to streamline feedback loop operations.

As a Senior Software Engineer at Ramp, I developed RLHF (Reinforcement Learning from Human Feedback) feedback loop systems to enhance the learning of AI-driven vendor negotiation agents. My work focused on integrating AI feedback into orchestration workflows, leveraging LLMs and custom guardrails for financial contract automation. Significant efforts involved orchestrating AI training data collection through asynchronous human-in-the-loop evaluations and LLM assessment tools. • Designed and implemented Ruby/Node.js AI communication clients for collecting annotated human feedback on LLM-generated contract recommendations. • Integrated Go safety guardrails and regex interceptors to ensure quality and safety of training data. • Automated data collection pipelines enabling AI retraining iterations using RLHF methodologies. • Leveraged internal proprietary AI tooling and Python orchestration services to streamline feedback loop operations.

2025 - 2026

Senior Backend Engineer - Data Labeling for Model Training (Plaid)

TextClassification
At Plaid, I spearheaded backend ML classifier integration and feature engineering for real-time ACH risk scoring, focusing on generating high-quality training datasets from live financial transactions. AI model development was supported by intensive data normalization, schema validation, and cross-validation using annotated risk event corpora. Data labeling was integral for supervised learning in XGBoost/LightGBM frameworks, ensuring accurate model predictions. • Built and managed pipeline for annotating risk events and transaction outcomes using Python FastAPI services. • Supported continual annotation and evaluation workflows with Pandas, NumPy, and internal tools. • Ensured consistency of labeled features via Go/Protobuf normalization and event-sourced state machines. • Partnered with data science teams to improve risk label taxonomy and data annotation quality for AI model retraining.

At Plaid, I spearheaded backend ML classifier integration and feature engineering for real-time ACH risk scoring, focusing on generating high-quality training datasets from live financial transactions. AI model development was supported by intensive data normalization, schema validation, and cross-validation using annotated risk event corpora. Data labeling was integral for supervised learning in XGBoost/LightGBM frameworks, ensuring accurate model predictions. • Built and managed pipeline for annotating risk events and transaction outcomes using Python FastAPI services. • Supported continual annotation and evaluation workflows with Pandas, NumPy, and internal tools. • Ensured consistency of labeled features via Go/Protobuf normalization and event-sourced state machines. • Partnered with data science teams to improve risk label taxonomy and data annotation quality for AI model retraining.

2023 - 2025

Junior Developer - Document Entity Annotation for Privacy AI (WireWheel)

DocumentEntity Ner Classification
As a Junior Developer at WireWheel, I was responsible for building a Python classification service for detecting and labeling personally identifiable information (PII) in enterprise documents. My work focused on automating PII detection using regex patterns and spaCy NLP in large batches of AWS S3 and RDS document data. Annotation pipelines were built to label document entities for downstream privacy workflows and risk assessment models. • Developed automated document labeling system for PII detection using Python and spaCy NLP. • Created annotation pipelines to integrate labeled data into Privacy Impact Assessment workflows. • Leveraged gRPC service integration and internal tools for managing document labeling jobs. • Enhanced accuracy of PII labels, reducing false positives in production environments.

As a Junior Developer at WireWheel, I was responsible for building a Python classification service for detecting and labeling personally identifiable information (PII) in enterprise documents. My work focused on automating PII detection using regex patterns and spaCy NLP in large batches of AWS S3 and RDS document data. Annotation pipelines were built to label document entities for downstream privacy workflows and risk assessment models. • Developed automated document labeling system for PII detection using Python and spaCy NLP. • Created annotation pipelines to integrate labeled data into Privacy Impact Assessment workflows. • Leveraged gRPC service integration and internal tools for managing document labeling jobs. • Enhanced accuracy of PII labels, reducing false positives in production environments.

2016 - 2020

Education

S

Stratford University

Master of Science, Information System

Master of Science
2013 - 2015
U

University of Toronto

Bachelor of Science, Computer Science

Bachelor of Science
2003 - 2007

Work History

R

Ramp

Senior Software Engineer

Remote
2025 - 2026
P

Plaid

Senior Backend Engineer

Remote
2023 - 2025