Sai Charan Pinjala

About Sai Charan Pinjala

I’m a Software Engineer with 3+ years of experience building scalable, cloud-native applications across enterprise and financial systems. I enjoy solving complex problems and turning ideas into reliable, production-ready solutions—especially in areas where backend engineering meets AI. In my recent work, I’ve focused on designing microservices and event-driven systems that handle real-time data at scale. I’ve built AI-powered solutions using LLMs, LangChain, and OpenAI APIs—most notably delivering a customer support automation platform that reduced manual effort by 45%. I’ve also contributed to fraud detection systems that improved detection rates by 35%, helping strengthen security in high-stakes environments. I’m comfortable working across the stack, from building responsive user interfaces with React to developing robust backend systems with Node.js, Python, and Java. I also have hands-on experience with AWS, Docker, Kubernetes, and CI/CD pipelines, ensuring applications are not just built well—but deployed and scaled efficiently. I thrive in collaborative, Agile teams and value clean code, thoughtful design, and continuous learning.

Experience

1. Software Engineer

   DXC Technology

   Feb 2026 — Present

   At DXC Technology, I’ve been focused on building intelligent, scalable systems that bridge AI and enterprise engineering. One of my key contributions has been architecting an LLM-driven customer support automation platform using LangChain and OpenAI APIs. By enabling contextual query resolution with vector databases, we reduced manual support effort by 45%, significantly improving response time and customer satisfaction.I also designed and implemented event-driven microservices using Node.js and Kafka, enabling real-time data processing across distributed systems. This improved system responsiveness and allowed seamless integration between services in a cloud-native environment.On the MLOps side, I built end-to-end pipelines using MLflow, Docker, and Kubernetes, which streamlined model deployment and monitoring—cutting release cycle time by 30%. Additionally, I introduced observability frameworks using Prometheus and Grafana, helping proactively detect bottlenecks and improve system reliability.

Education

• Purdue University Northwest

  Master of Science - MS, Computer Science

• Sri indu college of engineering and technology

  B.tech, Computer science and engineering

  2019 — 2023