Summary
Overview
Work History
Education
Skills
Work Availability
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Quote
Timeline
SeniorSoftwareEngineer

Christian Castro

Brownsville,TX

Summary

Dynamic Data Scientist and AI/ML Engineer with expertise in machine learning, statistical analysis, and data modeling. Proficient in Python, R, SQL, and cloud technologies like AWS, and Azure. Proven track record of designing and deploying advanced AI models that improve operational efficiency by 25%, and reduce processing time by 30%. Skilled in leading teams to integrate AI solutions into scalable architectures, driving business growth, and optimizing decision-making through actionable insights.

Overview

10
10
years of professional experience
2
2
years of post-secondary education

Work History

Senior AI/ML Engineer | Data Scientists

Aptean
01.2024 - Current
  • Designed and deployed advanced machine learning models leveraging TensorFlow, Keras, and Scikit-learn, resulting in a 25% improvement in prediction accuracy and a 30% reduction in data processing time.
  • Spearheaded the integration of AI-driven automation into scalable microservices architectures using Python, Golang, and Kubernetes, improving response times by 30% and optimizing operational efficiency.
  • Developed and optimized Large Language Models (LLMs) for applications such as chatbots, recommendation engines, and natural language workflows, significantly enhancing performance and task efficiency.
  • Engineered and maintained robust RESTful APIs using FastAPI, Flask, and API Gateway, enabling seamless integration of AI models with enterprise systems and third-party applications, enhancing overall system interoperability.
  • Utilized cloud technologies including AWS Lambda, SageMaker, Fargate, and Azure Kubernetes Service (AKS) to deploy machine learning models, accelerating deployment times by 40% while ensuring scalability and reliability.
  • Built scalable LLM data ingestion pipelines that integrated structured, unstructured, and streaming datasets, alongside implementing comprehensive ETL processes to ensure seamless data retrieval and efficient model training.
  • Delivered cutting-edge Natural Language Processing (NLP) solutions, including sentiment analysis, text summarization, and named entity recognition using Hugging Face Transformers, driving improvements in customer workflows and satisfaction metrics.
  • Applied advanced statistical methods such as regression analysis, hypothesis testing, and probabilistic modeling, optimizing data preprocessing, feature engineering, and model evaluation for more accurate insights.
  • Optimized cloud infrastructure for AI workloads with Apache Spark, achieving a 20% reduction in resource usage while ensuring high availability and performance.
  • Developed real-time KPI monitoring systems, integrating machine learning-based anomaly detection to enhance operational efficiency, enabling proactive decision-making.
  • Led cross-functional collaborations with data scientists, engineers, and product managers to align AI capabilities with business objectives, driving impactful solutions and measurable results.
  • Automated the model deployment process through CI/CD pipelines utilizing Jenkins, GitHub Actions, and Terraform, ensuring rapid iterations and minimizing downtime during updates.
  • Maintained and optimized large-scale databases, ensuring structured, high-quality data accessibility, which is critical for supporting AI applications and machine learning workflows.
  • Refined and retrained models using MLOps pipelines to maintain accuracy and adaptability to evolving data trends, ensuring optimal performance over time.
  • Presented technical insights to stakeholders in a clear and accessible manner, simplifying complex concepts surrounding Generative AI, SaaS architectures, and big data integration, aligning them with strategic business objectives.

Data Scientist | AI/ML Engineer

Cigna Healthcare
01.2021 - 12.2023
  • Led a cross-functional team of 5 engineers in the development and deployment of backend systems using .NET Core and C#, creating scalable microservices and APIs to support AI-based real-time applications and improve operational efficiency.
  • Designed and implemented AI pipelines for data preprocessing, feature engineering, and model training, boosting operational efficiency by 25% and ensuring the seamless integration of machine learning workflows.
  • Mentored junior developers, providing technical guidance on integrating NLP algorithms for tasks such as text classification, sentiment analysis, and keyword extraction using spaCy, which enhanced user interactions and automation.
  • Developed and fine-tuned large language models (LLMs) for enterprise AI applications, implementing model enhancements such as parameter optimization, prompt engineering, and retrieval-augmented generation (RAG) to boost accuracy and contextual understanding.
  • Built secure and scalable GraphQL and RESTful APIs, ensuring seamless integration of machine learning models with web and mobile applications to support AI-driven real-time solutions.
  • Led cloud optimization initiatives for AI systems using Azure Machine Learning and DynamoDB, achieving a 20% cost reduction while maintaining high performance and scalability.
  • Spearheaded the adoption of Apache Airflow for data pipeline orchestration and Azure Data Factory (ADF) for large-scale data integration, streamlining workflows and enhancing collaboration across teams.
  • Engineered AI-driven conversational intelligence systems, improving sentiment analysis, intent recognition, and contextual understanding in customer interactions to drive personalized engagement and improve user experience.
  • Integrated natural language understanding (NLU) with ASR and TTS technologies, enabling real-time auto-dialog generation for customer service automation and virtual assistants.
  • Developed AI-based customer-agent routing systems, leveraging Kore AI and Google CCAI to optimize query handling and improve response efficiency in large-scale contact centers.
  • Evaluated and retrained AI models through version-controlled workflows, establishing standardized processes that ensured accuracy and adaptability in dynamic environments.
  • Engineered voice/chatbot solutions with real-time analytics and callback-as-a-service platforms, reducing operational costs while enhancing customer engagement.
  • Collaborated with cross-functional teams, including ML engineers, data scientists, and cloud architects, to design and deploy end-to-end AI solutions, from data ingestion to real-time inference.
  • Championed compliance with HIPAA and GDPR, leading efforts to implement secure data practices that enhanced trust and security in AI solutions.
  • Presented technical insights to stakeholders, aligning AI-driven projects with business objectives and driving impactful decisions that optimized performance and organizational efficiency.

Data Scientist | Data Analytics Specialist

Amazon
12.2018 - 11.2020
  • Developed and deployed machine learning models for predictive analytics and anomaly detection using TensorFlow, Keras, and PyTorch, driving data-driven decision-making.
  • Built scalable ETL pipelines with AWS SageMaker, Glue, DynamoDB, and Azure Data Factory, optimizing data processing workflows.
  • Designed AI workflows for data ingestion, feature engineering, model training, and real-time deployment using Kubeflow, ensuring continuous integration and efficient AI deployment.
  • Delivered NLP solutions such as chatbot systems, text summarization, and named entity recognition with spaCy and Hugging Face Transformers to enhance automation and user experience.
  • Collaborated with engineering teams to deploy AI systems into production, ensuring high availability and low latency.
  • Utilized cloud technologies like AWS Lambda, S3, Fargate, and Azure Kubernetes Service (AKS) for scalable and cost-effective deployment.
  • Implemented automated testing for data pipelines using GitLab CI/CD, reducing deployment errors and enhancing reliability.
  • Developed Python and Bash scripts to automate system health checks and log monitoring, ensuring optimal system performance.

AI Solutions Architect | Machine Learning Engineer

Duke Energy
08.2017 - 11.2018
  • Spearheaded the development of a robust and scalable application using Vue for the front-end and Laravel for the back-end, ensuring high performance and scalability.
  • Managed the deployment of an AWS DocumentDB environment using Terraform, establishing a scalable and efficient infrastructure setup.
  • Authored AWS Lambda functions to automate the ingestion of historical data into the database, ensuring data consistency and accuracy for new projects.
  • Developed a file ingestion system utilizing PySpark and API integration, streamlining data integration from Redshift, AuroraDB, and AWS Delta Lake, reducing processing time and improving data quality.
  • Worked with multiple AWS services such as Kafka, Hadoop, AWS-EMR, EC2, Kinesis, S3, QuickSight, DynamoDB, RedShift, RDS, AWS Glue, and Lambda to efficiently manage and process large datasets.
  • Revamped database infrastructure using SQL and PL/SQL, enhancing system efficiency, accelerating data retrieval, and improving user satisfaction.
  • Optimized data pipelines with Apache Kafka and Flink, boosting query performance and reducing processing time, improving overall system reliability.
  • Designed SSIS packages to ETL data from XML files, enhancing integration and improving data accuracy and consistency.
  • Implemented CI/CD pipelines in GitLab, automating deployment processes for data pipelines and infrastructure, ensuring faster, more reliable releases.
  • Applied data visualization best practices using Tableau and Power BI, delivering actionable insights and improving data comprehension for cross-functional teams.

Data Analyst

OPN LAB
09.2015 - 06.2017
  • Utilized SAS and SAS Enterprise Miner for advanced statistical analysis and predictive modeling, resulting in a 15% improvement in forecasting precision.
  • Conducted extensive data analysis using Python, R, SQL, and Java to extract, clean, and transform large datasets, enabling actionable insights for business decision-making.
  • Implemented robust data cleansing and quality assurance processes, ensuring data sources were accurate, reliable, and up-to-date, resulting in reduced errors and enhanced data trustworthiness.
  • Employed advanced data visualization tools, including Tableau, Power BI, and QlikView, to create insightful dashboards and reports that supported strategic decision-making.
  • Applied scripting languages such as HTML and CSS for web-based data presentation and analysis, improving accessibility and user interaction.
  • Leveraged cloud platforms like AWS, Azure, and GCP to manage, process, and analyze large-scale datasets, significantly enhancing scalability and operational efficiency.

Education

Bachelor of Science - Computer Science

University of North Texas
Denton, TX
04.2013 - 08.2015

Skills

Programming languages: Python, SQL, Scala, Presto SQL, C,

MATLAB, NoSQL, Java

Statistical Methods: Hypothetical Testing, ANOVA, Time Series, and Statistics

Big Data and Cloud: Apache Kafka, Alteryx, Databricks, Hadoop, Spark, Flink, Hive, MapReduce, Apache Spark, Pig, BigQuery, AWS S3, Azure, Azure Data Explorer, and WordPress

Packages: NumPy, Pandas, Matplotlib, SciPy, Scikit-learn,

Seaborn, TensorFlow, PySpark, SQLAlchemy, OpenPyXL

Data Processing Tools: Apache Airflow, Apache NiFi, Talend, Bash, Informatica

Data Warehousing: Snowflake, Google BigQuery, Apache Hive, Azure Cloud Services, Azure Databricks, AWS (EC2, RDS, S3,

Lambda, Redshift, Athena, Glue)

Containerization: Docker, Kubernetes, Jenkins, Git, SVN

Data Visualization: Tableau, Power BI, SSRS, SSIS, MATplotib

Databases: MySQL, MongoDB, PostgreSQL, Firebase, and Neo4J

Data Modeling: ER Diagrams, Dimensional Modeling, Star Schema, Data Vault

Machine Learning: Regression analysis, Bayesian Method, Decision Tree, Random Forests, Support Vector Machine, Neural Network, K-Means, KNN, SVM, Naive Bayes, NLP, CNN, Deep Learning, Predictive Models, MLOps, and Linux Development

Methodologies: SDC, Agile/Scrum, API Integration, Waterfall, Ignition Troubleshooting

Other Skills: Django, MS Office, Atlassian Jira, Confluence,

PeopleSoft, CI/CD, ALM, Postman, Data Cleaning, Data Wrangling, Critical Thinking, CI/CD Pipelines, Communication Skills, Presentation Skills, Problem-Solving, Salesforce,

A/B Testing Analysis, ETL, Automated test scripts, UI/UX, React

Work Availability

monday
tuesday
wednesday
thursday
friday
saturday
sunday
morning
afternoon
evening
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Work Preference

Work Type

Full TimePart TimeContract Work

Work Location

Remote

Important To Me

Work-life balanceCompany CultureFlexible work hours

Quote

Imagination is everything. It is the preview of life’s coming attractions.
Albert Einstein

Timeline

Senior AI/ML Engineer | Data Scientists

Aptean
01.2024 - Current

Data Scientist | AI/ML Engineer

Cigna Healthcare
01.2021 - 12.2023

Data Scientist | Data Analytics Specialist

Amazon
12.2018 - 11.2020

AI Solutions Architect | Machine Learning Engineer

Duke Energy
08.2017 - 11.2018

Data Analyst

OPN LAB
09.2015 - 06.2017

Bachelor of Science - Computer Science

University of North Texas
04.2013 - 08.2015

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Christian Castro