Phanidhar Venkata Naga Kasuba
Data Engineer @Saayam For All
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WORK HISTORY
Data Engineer @Saayam For All
US
Architect and maintain end-to-end ETL pipelines using Python, PySpark, and SQL to transform raw data into structured storage systems- Execute high-throughput data processing for 10M+ daily transaction rows to ensure high availability for downstream machine learning datasets- Manage cloud-native data assets within AWS S3, organizing and optimizing file access as part of a live engineering pipeline process- Develop generative AI workflows and ensemble predictive models, achieving 75% accuracy in risk signal detection for clinical-grade screenings- Implement NLP algorithms and text classification to transform unstructured records into structured sentiment data and insights- Apply SHAP-based explainability to translate complex model outputs into interpretable insights for clinicians and caregivers- Automate data cleaning and preparation tasks using custom Python scripts, building scalable infrastructure that reduces processing latency- Construct real-time computer vision pipelines using YOLOv8 and OpenCV, integrated with Apache Kafka for high-throughput streaming- Pioneer micros-ervices architectures for model serving, integrating machine learning models into live dashboards to drive real-time user engagement- Perform rigorous data quality and statistical validation checks to ensure model reliability and robustness for high-stakes decision-making- Identify operational cost-driver anomalies through strategic analysis, delivering insights that lead to reduction in monthly spend- Create summary dashboards and interactive reports in Power BI, Tableau, and Looker Studio to deliver actionable business intelligence- Containerize reporting applications and ML pipelines using Docker to facilitate seamless deployment across cloud-native environments.
EDUCATION
Mahatma Gandhi Institute of Technology
Bachelor of Technology - BTech, ECE
Webster University
Master of Science - MS, Data Analytics
ABOUT PHANIDHAR VENKATA NAGA KASUBA
Data Engineer and Machine Learning Engineer with 5+ years of experience building cloud-based data pipelines, analytics layers, and ML-enabled systems that move from raw data to trusted outputs. Strong focus on Azure-first engineering, scalable ETL/ELT design, and production-ready workflows using Python and SQL.Experienced in designing data models, enforcing data quality through validation rules, and improving reliability through clear documentation, monitoring-friendly structure, and CI/CD-ready pipeline practices. Comfortable working across BI and analytics delivery as needed, including Power BI and Tableau, with an emphasis on consistent metrics and performance-aware reporting.Hands-on ML engineering exposure across predictive modeling, feature pipelines, and practical NLP foundations, including retrieval-augmented generation concepts and vector database workflows. Preference for systems that are testable, interpretable, and maintainable in real environments, not just notebook experiments.Developer and lead contributor to NeuroScreen, a research-grade AI Parkinson’s risk screening application selected/presented in a Translational Science conference track in 2026.Currently based in California and open to relocation. Actively building and scaling data platforms with a focus on performance, reliability, and real-world impact.Core SkillsGenerative AI: LLMs, RAG, LangChain, Transformers, Hugging Face, OpenAI API, Prompt Engineering, Vector DBs (Pinecone)Deep Learning & Frameworks: TensorFlow, Keras, PyTorch, CNN (OpenCV), RNN, LSTM, Neural NetworksMachine Learning: Regression, Classification, Clustering, Time series Analytics, Scikit-learnLanguages & Libraries: Python, SQL, NoSQL (MongoDB), PySpark, FastAPI, R, BeautifulSoupData Analysis & Stats: Hypothesis Testing, Inferential Statistics, EDA, A/B Testing, Matplotlib, SeabornVisualization & BI: Power BI, Tableau, Looker Studio, DAX, Data StorytellingCloud & DevOps: AWS, Google Cloud, Docker, Git, MLflow, Airflow
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