AI & MLOps Data Engineer
Resume:
PROFILE
Artificial Intelligence Engineer with ** years of experience designing machine
learning solutions, data platforms, and AI-driven applications across digital media, healthcare, insurance, and consulting environments. Experienced in developing AI models using Python, TensorFlow, Keras, and PyTorch with expertise in NLP, computer vision, statistical modeling, and cloud AI platforms. Skilled in deploying scalable machine learning pipelines, optimizing model performance, and collaborating with cross-functional engineering teams. EXPERIENCE
Conde Nast 07/2021 - 12/2025
AI Data Engineer
• Designed artificial intelligence solutions for digital media platforms using Python, TensorFlow, Keras, and PyTorch to build machine learning algorithms that improved content intelligence, prediction accuracy, and automated data-driven decision processes.
• Developed Natural Language Processing models with Scikit-learn, Deep Learning, and Feature Engineering techniques to analyze large-scale publishing datasets while supporting scalable AI Model Deployment workflows across Conde Nast technology environments.
• Implemented Computer Vision applications using TensorFlow and Keras for media asset analysis, applying Statistical Modeling approaches and Model Optimization strategies to improve reliability, performance, and operational efficiency within digital platforms.
• Built MLOps workflows with Docker, Kubernetes, and AWS SageMaker to automate AI model lifecycle management, enabling repeatable deployment pipelines and supporting enterprise-grade artificial intelligence operations for Conde Nast media products.
• Created Data Pipelines using Apache Spark, Apache Airflow, and ETL processes to prepare structured and unstructured information for machine learning initiatives supporting Conde Nast content distribution and analytics capabilities.
• Configured Google Cloud AI Platform and Google Cloud Platform resources to develop scalable AI services, integrating cloud-based infrastructure with Python applications and machine learning workflows for digital publishing operations.
• Applied SQL, Java, and Scala programming expertise to engineer high-volume data solutions, optimize processing frameworks, and support AI applications requiring dependable analytics and machine learning functionality across media systems.
• Integrated Generative AI concepts into experimental applications while evaluating Allan Hoang
Artificial
Intelligence
Engineer
CONTACT
***********@*****.***
(518) 241 - 3803
Pembroke Pines, FL 33028
SKILLS
Programming
• Python
• SQL
• Java
• Scala
AI Frameworks
• TensorFlow
• Keras
• PyTorch
• Scikit-learn
Machine Learning
• Machine Learning
Algorithms
• Statistical Modeling
• Deep Learning
• Model Optimization
• Feature Engineering
AI Development
• Natural Language
Processing
• Computer Vision
• Generative AI
• AI Model Deployment
• MLOps
Cloud Platforms
• AWS SageMaker
• Google Cloud AI Platform
• AWS
• Google Cloud Platform
emerging artificial intelligence trends, improving automation opportunities and enhancing intelligent features within Conde Nast technology solutions.
• Managed Data Warehousing environments and Tableau dashboards to provide analytical visibility, combining SAS capabilities with machine learning outputs for business intelligence and performance measurement across publishing operations.
• Applied the Astronomer Certification for Apache Airflow 2 Fundamentals knowledge to improve workflow orchestration practices, maintaining reliable scheduling patterns for AI and data engineering pipelines used by Conde Nast teams.
• Leveraged SAS - CSU Global Academic Specialization in Applied Data Analytics and SAS - CSU Global Academic Specialization in Business Intelligence and Performance Management to strengthen analytical modeling and reporting practices.
• Collaborated with engineering, product, and analytics teams to communicate AI model results, solve complex technical challenges, and deliver reliable artificial intelligence solutions aligned with Conde Nast digital media requirements. Clearsense, LLC 01/2020 - 07/2021
Machine Learning Engineer
• Developed machine learning applications for healthcare technology platforms using Python, TensorFlow, Keras, and PyTorch to create predictive models that supported data-driven improvements across Clearsense healthcare analytics solutions.
• Built Natural Language Processing workflows with Scikit-learn and Feature Engineering methods to transform healthcare information into structured insights while applying Statistical Modeling techniques for accurate analytical outcomes.
• Created Computer Vision prototypes using Deep Learning frameworks to evaluate image-based healthcare datasets, optimizing model behavior through Model Optimization techniques and performance validation processes.
• Deployed AI Model Deployment solutions using AWS SageMaker, Docker, and Kubernetes to support scalable artificial intelligence services and maintain reliable machine learning applications in healthcare data environments.
• Engineered ETL processes, Data Pipelines, and Apache Spark workloads to prepare healthcare datasets for AI initiatives while maintaining efficient data movement across enterprise Data Warehousing architectures.
• Implemented Apache Airflow workflows using knowledge from the Astronomer Certification for Apache Airflow 2 Fundamentals to coordinate automated machine learning and analytics processing schedules.
• Utilized SQL, Java, and Scala programming skills to enhance backend data
• Docker
• Kubernetes
Data Engineering
• Apache Spark
• Apache Airflow
• ETL
• Data Pipelines
• Data Warehousing
Analytics
• SAS
• Tableau
• Astronomer Certification for
Apache Airflow 2
Fundamentals
• SAS - CSU Global Academic
Specialization in Applied Data
Analytics
• SAS - CSU Global Academic
Specialization in Business
Intelligence and Performance
Management
EDUCATION
Colorado State University
Master's degree in Data
Analytics
2021
The University of Central
Florida
Bachelor's degree in
Information Technology
2017
services, improve processing performance, and integrate machine learning functionality into Clearsense healthcare software systems.
• Applied AWS, Google Cloud Platform, and Google Cloud AI Platform resources to evaluate cloud-based AI architectures, enabling scalable solutions for healthcare analytics and intelligent application development.
• Produced Tableau and SAS analytical reports that combined AI-generated insights with business intelligence metrics, supporting healthcare stakeholders with accurate performance measurements and operational reporting capabilities.
• Applied SAS - CSU Global Academic Specialization in Applied Data Analytics and SAS - CSU Global Academic Specialization in Business Intelligence and Performance Management to improve analytical methodologies. Florida Blue 11/2018 - 01/2020
Data Engineer
• Developed Python and SQL data solutions for insurance technology systems, creating reliable Data Pipelines and ETL processes that supported analytics operations and improved information availability across Florida Blue platforms.
• Built Apache Spark processing workflows and Data Warehousing solutions to manage large insurance datasets while applying Machine Learning Algorithms and Statistical Modeling techniques for predictive analytics initiatives.
• Implemented TensorFlow, Keras, and Scikit-learn experiments to evaluate artificial intelligence opportunities, supporting AI Model Deployment concepts and improving automated analysis capabilities within insurance systems.
• Created Tableau dashboards and SAS reporting solutions to visualize operational metrics, combining analytics outputs with business intelligence requirements for Florida Blue insurance technology stakeholders.
• Managed Apache Airflow scheduling workflows and applied Astronomer Certification for Apache Airflow 2 Fundamentals knowledge to maintain dependable automated data processing pipelines.
• Integrated AWS and Docker technologies into development workflows, supporting scalable application environments and improving deployment consistency for enterprise data engineering solutions.
• Used Java and Scala programming languages alongside SQL to enhance data processing services, optimize system performance, and maintain reliable integration between insurance applications and analytical platforms.
• Applied SAS - CSU Global Academic Specialization in Applied Data Analytics and SAS - CSU Global Academic Specialization in Business Intelligence and Performance Management to strengthen reporting and analytical practices. Ernst & Young 07/2017 - 11/2018
Analytics Engineer
• Developed SQL, Python, and SAS analytics solutions for consulting projects, transforming client datasets into structured insights through ETL processes, Data Pipelines, and Data Warehousing implementations.
• Created Tableau visualizations and analytical reporting frameworks using SAS - CSU Global Academic Specialization in Applied Data Analytics knowledge to support consulting teams with measurable business intelligence outcomes.
• Applied Machine Learning Algorithms, Feature Engineering, and Statistical Modeling techniques to evaluate complex datasets while supporting data-driven recommendations for Ernst & Young consulting engagements.
• Built Apache Spark and Apache Airflow workflows to improve data processing efficiency, applying Astronomer Certification for Apache Airflow 2 Fundamentals concepts within analytics automation activities.
• Utilized AWS, Google Cloud Platform, and Docker technologies to support scalable analytics environments, improving data solution delivery and technical consistency across Ernst & Young projects.
• Applied SAS - CSU Global Academic Specialization in Business Intelligence and Performance Management expertise to enhance analytical reporting quality and provide clients with actionable performance insights.
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