Ramya Ganesh

About Ramya Ganesh

I’m a Software Engineer and Master’s candidate at Carnegie Mellon University, graduating this December 2025, with 3+ years of experience designing cloud-native platforms, building real-time distributed systems, and enabling machine learning at scale across healthcare and logistics domains.At Philips Healthcare, I worked as a full stack engineer on modernizing clinical imaging systems. I developed scalable frontend interfaces using TypeScript and Angular, built distributed backend services with.NET Core and AWS, and integrated streaming pipelines with Kafka and Kinesis to support AI-powered diagnostics in production environments.As part of my capstone at CMU, I led the development of a multi-tenant real-time data platform on Azure. The platform includes secure, schema-aware ingestion with Kafka and Debezium, Snowflake-based analytics, and robust pipelines for training and inference workflows.I have also worked on applied AI projects such as LoRA-based fine-tuning on GPT-2, extending Stable Diffusion for multi-object generation, and improving transformer efficiency using RoPE and GQA.I am especially interested in the intersection of cloud infrastructure, distributed systems, and ML engineering. My goal is to build scalable and future-ready platforms that bring machine learning into real-world applications with reliability and impact.

Experience

1. Software Engineer

   Eparts Services Llc

   Jan 2025 — Present · Pittsburgh, PA, US

   Led a cross-functional team of 6 in architecting a multi-tenant cloud-native Data Warehouse on Azure, harnessing Snowflake for real-time analytics and cutting down reporting latency by 60% across ∼50K+ records/day across ∼15+ tenants.• Architected fault-tolerant Real-time ELT pipelines (Kafka, Debezium) on Azure Kubernetes Service (AKS) to stream ∼150K+ SQL Server CDC events/day into Snowflake, achieving 85% improvement in data freshness and enabling scalable ML feature engineering.• Unified advanced observability using Snowflake query logs, Grafana dashboards, and Prometheus metrics, decreasing incident triage time by 50% and ensuring >99.9% SLA adherence for ∼75+ critical data workflows.

Education

• PSG College of Technology

  Bachelor of Engineering, Biomedical/Medical Engineering

• Amrita Vishwa Vidyapeetham

  Master of Technology - MTech, Artificial Intelligence and data science

• Carnegie Mellon University

  Master of Science - MS, Computer Software Engineering

  2024 — 2025