Saipriya Akula

About Saipriya Akula

Data & AI Engineer with 5+ years of experience building high-scale pipelines, cloud-native architectures, and ML-driven systems across top financial institutions. My work blends strong systems engineering, deep data expertise, and hands-on AI/ML experience—enabling me to deliver solutions that are fast, reliable, and built for enterprise scale.Obtained a Master\'s Degree in Computer Science at the University of Central Missouri, I focus on distributed systems, machine learning, and cloud data engineering. This academic foundation strengthens my ability to design data and AI platforms that support real-time decision-making and complex analytics.During my time at Capital One, I delivered ML and RAG-focused data pipelines processing billions of records, improved retrieval accuracy and latency, automated labeling workflows, and ensured 99.8% pipeline availability—directly accelerating model development across multiple teams.At Infosys, I engineered AWS and Snowflake ecosystems with Glue, Lambda, Step Functions, Spark, and Kafka/Kinesis. I built reusable data quality frameworks, optimized ELT workloads, and led near–real-time ingestion to support finance, risk, and analytics use cases. My work standardized CI/CD with Terraform and GitHub Actions, reduced processing costs, and strengthened data governance.Earlier at NTT Data, I built streaming and batch pipelines using Kafka, PySpark, AWS Glue, and Snowflake, while improving data quality, securing PII access, and implementing Airflow-based orchestration. I also contributed to observability, alerting, and environment stability across Linux-based systems.Across every role, I bring a systems-first mindset—leveraging Linux, Docker, Bash, CI/CD, and cloud platforms—to create predictable, scalable environments that support both data engineering and AI/ML workloads. I’m passionate about solving hard engineering problems, enabling teams with clean and reliable data, and building the infrastructure that powers next-generation AI.

Experience

1. Developer Platform Engineer (Ci Cd & Automation)

   Capital One

   Jun 2025 — Present

   I improved enterprise RAG performance by boosting retrieval accuracy 32% and cutting latency 45%, helping teams access insights faster. I build large-scale pipelines processing 2.5B+ records and deliver clean, production-ready datasets.I automated labeling workflows to reduce manual effort by 60% and accelerated experiment cycles 3×. I also maintain 99.8% pipeline uptime and drive storage optimizations that save 8+ TB per month, supporting scalable and efficient AI development.

Education

• University of Central Missouri

  Master's degree