About

The person behind the data.

Engineer, researcher, lifelong tinkerer. I turn messy, real-world data into systems that make decisions - and, when the tools don't exist yet, I build them.

Hi, I'm Lok - a GenAI engineer who came up through classical ML.

I'm a Generative AI Engineer and Data Science Consultant with 5+ years building end-to-end ML pipelines, data platforms, and LLM-powered applications for enterprise teams.

Currently finishing an MS in Business Analytics (Data Science) at the University of Texas at Dallas - 4.0 GPA, Dean's Excellence Scholarship. Before Dallas I spent four years shipping models and pipelines at Tredence and Honeywell, and published my first peer-reviewed paper while still an undergrad.

My work lives where data engineering, classical ML, and LLMs meet - Azure OpenAI, LangChain, MLflow, Databricks, PySpark, Airflow. I care about business outcomes, not benchmarks.

Dallas, TX Open to relocation Full-time & consulting 5+ years

Work experience

4 roles · 5+ years
Jan 2024 - Present
Generative AI Engineer
Vanguard · via Tech Metro · Dallas, TX
Building LLM-powered solutions and GenAI applications for one of the world's largest investment management firms. Working with Azure OpenAI, LangChain, vector databases, and MLflow for model lifecycle management across production RAG systems.
Jul 2021 - Jul 2023
Machine Learning Engineer
Tredence Analytics · India
Delivered strategic data science work for Walmart and other enterprise clients - a 12% reduction in campaign cycle time and 17% delivery cost reduction for Ecolab. Built Python/Airflow automation pipelines that cut manual effort by 20%.
May 2019 - Jun 2021
Machine Learning Engineer
Honeywell · India
Developed predictive models and ML solutions for industrial IoT. Applied deep learning and statistical modelling to sensor data streams for anomaly detection and predictive maintenance on connected equipment.
2019 · Summer
Business Analyst Intern
Mega Shots Internet · India
Reduced marketing costs by 35% through customer behavior prediction models. Built customer segmentation and churn prediction pipelines in Python with scikit-learn.

Education

3 degrees · 4.0 at UTD
Aug 2023 - May 2025
MS in Business Analytics · Data Science
University of Texas at Dallas · Richardson, TX
4.0 GPA · Dean's Excellence Scholarship · Graduate Certificate in Applied Machine Learning. Focus on advanced ML, deep learning, and large-scale analytics.
Jan 2022 - Dec 2023
Executive MS in ML & AI
Liverpool John Moores University · UK (Online)
Advanced coursework in deep learning, NLP, computer vision, reinforcement learning, and AI ethics - pursued alongside full-time ML engineering work.
Aug 2017 - Jan 2021
B.Tech · Computer Science & Engineering
Kalasalingam University · India
Founded and served as President of the Kalasalingam Data Science Club. Published my first peer-reviewed research paper (Springer Proceedings) during undergrad.

The trophy wall

6 certifications · all verified
DB
Databricks
Generative AI Engineer Associate
LLM-enabled solutions, Vector Search, Model Serving, MLflow, Unity Catalog, and production RAG on the Databricks Lakehouse.
Verify ↗
DB
Databricks
Data Engineer Associate
ELT pipelines, Apache Spark SQL, Delta Lake, Auto Loader, Delta Live Tables, governance - production data engineering workflows.
Verify ↗
DB
Databricks
Machine Learning Associate
ML lifecycle, data prep, model training, MLflow Tracking, Model Serving, end-to-end ML experiments on the Databricks Lakehouse.
Verify ↗
MS
Microsoft
Azure Data Engineer Associate
Data integration and transformation with Azure Data Factory, Synapse, Databricks, and Blob Storage - plus governance and compliance.
Verify ↗
MS
Microsoft
Azure Data Scientist Associate
ML workloads on Azure ML workspace, AutoML, responsible AI, model deployment to endpoints, and continuous monitoring of production models.
Verify ↗
UT
UT Dallas
Graduate Certificate · Applied ML
Supervised & unsupervised learning, model evaluation, feature engineering, ensembles, neural nets - with practical Python, scikit-learn, and TensorFlow work. Awarded alongside the MS.
Awarded 2025

Peer-reviewed research

2 papers
01

Employee Attrition Prediction Using Machine Learning Algorithms

Applied ML to forecast workforce departures with 98% accuracy. Analyzes HR datasets to identify key turnover patterns and at-risk employees, enabling preventive retention strategies. Covers feature engineering, model comparison across multiple algorithms, and practical deployment considerations for HR analytics.

Springer Lecture Notes in Networks and Systems · Vol. 286 · DOI 10.1007/978-981-16-5120-5_44
Read on Springer ↗
02

A Concise Overview of Artificial Intelligence for Research and Development

Structured review of how AI is transforming research and development - covering robotics, knowledge-based systems, machine learning, natural language processing, and computer vision, along with the key obstacles in replicating human reasoning. A valuable entry-point reference for researchers and practitioners entering the field.

Research Review · AI principles in scientific R&D
Request paper

The toolkit

What I reach for
Languages
Python R SQL PySpark Scala (Spark)
ML & AI
scikit-learn PyTorch TensorFlow LangChain Azure OpenAI MLflow Hugging Face XGBoost
Data & Cloud
Databricks Delta Lake Azure Data Factory Azure Synapse Airflow Apache Spark Google Cloud Run
Analytics & BI
Power BI Tableau Amplitude AppsFlyer Omniture
Databases
Oracle IBM DB2 SQL Server PostgreSQL Spark SQL
Craft & ops
Git Docker FastAPI Streamlit CI/CD
Let's build something

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