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L
Lunalo L.

Lunalo L.

Senior Full-Stack / AI Engineer

USA flagN/A, Usa

Key Skills

Software

Other

Top Subject Matter

AI model evaluation
Grading Domain Expertise
and annotation workflows

Top Data Types

TextText

Top Task Types

ClassificationClassification
Entity (NER) ClassificationEntity (NER) Classification
Object DetectionObject Detection
RLHFRLHF
Fine-tuningFine-tuning
Data CollectionData Collection

Freelancer Overview

Senior Full-Stack / AI Engineer. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Doctor of Philosophy, Massachusetts Institute of Technology (MIT) (2026) and Master of Science, Massachusetts Institute of Technology (MIT) (2023). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Evaluation and Rating.

Labeling Experience

Senior Full-Stack / AI Engineer, NexusAI (2025 – Present)

Designed and deployed scalable backend systems supporting AI model evaluation workflows and distributed processing. Built RESTful APIs and full-stack applications used for real-time AI systems, including evaluation and prompt optimization efforts. Evaluated AI model outputs and contributed to model reliability initiatives through structured collaboration and feedback processes. • Improved API response performance and optimized backend throughput for real-time AI systems. • Collaborated on data-intensive systems requiring analytical review, debugging, and structured feedback. • Contributed to prompt optimization and model reliability through evaluation-driven iterations. • Developed tools and services to enable end-to-end evaluation workflow execution.

2025 - Present

Full-Stack Software Engineer, HandshakeAI (2024 – Present)

Built internal dashboards and evaluation tools for AI model assessment and workflow monitoring. Implemented scalable backend services and data pipelines supporting grading and annotation systems for AI workflows. Performed structured software evaluation tasks involving technical reasoning, debugging, and analysis to improve training/validation outcomes. • Developed caching and optimized data processing performance using SQL databases and asynchronous workflows. • Supported AI training and validation projects with high attention to detail and technical accuracy. • Collaborated with engineering teams to refine evaluation workflows and feedback loops. • Focused on improving the quality and reliability of model outputs via evaluation-driven iterations.

2024 - Present

Software Engineer, Scale AI (2023 – 2024)

Other

Engineered backend services and processing pipelines for large-scale AI data systems used in model development. Optimized distributed processing workflows to improve efficiency for evaluation and training-related data handling. Supported AI model training infrastructure and software evaluation initiatives to ensure dependable pipeline performance. • Collaborated on debugging, system optimization, and infrastructure reliability improvements for data pipelines. • Assisted evaluation efforts through tooling and infrastructure changes for large-scale AI data processing. • Implemented and maintained backend pipeline logic supporting downstream model training and evaluation tasks. • Worked within technical teams to iterate on performance bottlenecks and reliability issues.

2023 - 2024

Machine Learning Engineer, IBM (2022 – 2023)

Other

Developed and deployed NLP and machine learning applications used in production environments, including services supporting model serving and real-time processing. Built APIs and scalable services for model serving that support evaluation and reliability improvements. Improved AI pipeline reliability and performance through systematic testing and optimization of deployed components. • Worked with structured datasets and analytics workflows in support of ML application behavior. • Collaborated on cloud-based deployments and operational performance tuning. • Implemented testing/optimization routines to strengthen end-to-end pipeline stability. • Focused on production-grade readiness of ML services for ongoing model evaluation needs.

2022 - 2023

Education

M

Massachusetts Institute of Technology (MIT)

Doctor of Philosophy, Software Engineering

Doctor of Philosophy
2023 - 2026
M

Massachusetts Institute of Technology (MIT)

Master of Science, Software Engineering

Master of Science
2020 - 2023

Work History

N

NexusAI

Senior Full-Stack / AI Engineer

N/A
2025 - Present
H

HandshakeAI

Full-Stack Software Engineer

N/A
2024 - Present