Lawrence O.

Key Skills

Software

Other

Top Subject Matter

LLM evaluation and human preference data collection

Multimodal dataset engineering including spatial and medical (DICOM) data preparation

Automated AI evaluation pipelines and structured LLM assessment

Top Data Types

Text

Image

Document

Top Task Types

Data Collection

Fine-tuning

Freelancer Overview

Senior Full-Stack and AI Engineer in Contract Review, Compliance, and Legal Research. Brings 5+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Doctor of Philosophy, University of Nairobi (2026) and Master of Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT) (2023). AI-training focus includes data types such as Text, Medical, and DICOM and labeling workflows including Evaluation, Rating, and Data Collection.

Labeling Experience

Senior Full-Stack / AI Engineer | Handshake AI (2025 – Present)

Architected distributed backend services powering automated AI evaluation pipelines for structured model assessment. Developed asynchronous, low-latency REST APIs to support evaluation workflows and cached queue-backed background jobs. Designed structured model evaluation frameworks to improve LLM output accuracy, prompt durability, and security alignment. • Implemented Redis-backed queues and caching layers to reduce evaluation latency • Built evaluation APIs supporting high-throughput AI assessments • Defined structured evaluation frameworks for model output quality • Supported prompt and security alignment improvements for evaluated models

2025 - Present

Full-Stack Software Engineer | Outlier AI (2024 – Present)

Text

Built reactive internal dashboards for visualization of LLM training outputs and interactive human preference data collection. Tracked grading trends and created programmatic evaluation annotations to support ongoing model assessment. Optimized database and worker workflows to manage massive dataset volumes used for evaluation tasks. • Developed interactive preference collection workflows for human-in-the-loop assessment • Created scalable database schemas for evaluation annotation tracking • Implemented automated workflows to record grading trends • Performed profiling and benchmarking to remediate performance regressions

2024 - Present

AI Training & Technical Project Specialist | Freelance Remote Contractor (2023 – Present)

OtherData Collection

Engineered high-fidelity dataset projects and multimodal simulation models used for training AI systems. Built GIS mapping pipelines and terrain analysis overlays to generate structured spatial training data. Reconstructed 3D medical datasets from DICOM sources to support visualization and AI training preparation workflows. • Produced simulation-based and spatial data used for training multimodal AI • Developed QGIS-based terrain reconstruction and GIS overlay pipelines • Created custom ParaView visualization projects for dataset generation • Reconstructed 3D medical datasets from DICOM sources

2023 - Present

Software Engineer | Scale AI (2023 – 2024)

TextData Collection

Developed high-throughput data processing and orchestration pipelines for large machine learning training datasets. Implemented parallel processing to speed up heavy file parsing and dataset validation for training readiness. Built automation tooling to test, deploy, and monitor backend evaluation workloads supporting ML training operations. • Orchestrated processing pipelines for massive ML training datasets • Accelerated file parsing and dataset validation using parallel techniques • Created internal tooling for automated testing and deployment • Supported monitoring of evaluation workloads used in ML training

2023 - 2024

Machine Learning Engineer | IBM (2022 – 2023)

TextFine-tuning

Scaled machine learning model serving microservices in Kubernetes to support low-latency responses for NLP services. Integrated CI/CD routines for automated linting, unit testing, and deployment of natural language processing components. Cleaned, transformed, and managed enterprise-scale databases to provide refined assets for downstream training pipelines. • Operated and scaled NLP services to production for model serving • Automated testing and deployment via CI/CD practices • Performed data cleaning and transformations for training readiness • Managed enterprise databases to supply refined training pipeline assets

2022 - 2023

Education

Work History