Hello, I'm
Saikiran Reddy J
Master's Student in Computer Science
Stony Brook University
Focused on AI Systems, Distributed Systems, and Intelligent Agents
About Me
I am a Master's student in Computer Science at Stony Brook University, specializing in AI Systems, Distributed Systems, and Intelligent Agents. My work focuses on designing scalable infrastructure for deploying machine learning models and building fault-tolerant systems that integrate autonomous agents into production environments.
My research interests span AI infrastructure, autonomous agents, distributed systems design, and cloud-native DevOps. I am particularly interested in how intelligent agents can interact with APIs and workflows, consensus protocols for system reliability, and ML pipeline automation in cloud environments.
I have hands-on experience building distributed databases with Rust, implementing consensus algorithms like Paxos and Raft, and deploying ML applications using Kubernetes, Terraform, and CI/CD pipelines. Previously, I completed my Bachelor's in Computer Science from Sathyabama University in 2024.
Research Interests
AI Systems & ML Infrastructure
Designing scalable infrastructure for deploying machine learning models, including model serving, monitoring, and pipeline automation in cloud environments.
Autonomous Agents & Workflows
Exploring intelligent agents that interact with APIs, tools, and system workflows, enabling automation of complex multi-step tasks.
Distributed Systems
Building fault-tolerant systems using consensus protocols (e.g., Paxos), focusing on consistency, replication, and scalability.
Cloud & DevOps Systems
Leveraging Kubernetes, Terraform, and CI/CD pipelines to enable reproducible, scalable deployments of AI-driven applications.
Projects
NexusDB: Distributed MySQL Database System
Built a distributed SQL database in Rust with LSM-tree storage, Raft replication, and ACID transactions. Achieved 150K-180K rows/sec and reduced query latency by 35%.
Scalable Distributed Banking Application
Built a fault-tolerant distributed transaction system in Go using Modified Paxos and Two Phase Commit. Achieved 540+ TPS with 99.9% availability across 7 nodes, with leader election and failure recovery.
GitOps Cloud Deployment Framework
Built a GitOps-based infrastructure system using Terraform, Kubernetes, and CI/CD pipelines, reducing provisioning time by 50% and improving deployment reliability.
Experience
Software Engineering Assistant
Stony Brook University | Aug 2025 - Present
- Reduced shelf damage incidents by 35% and eliminated lane blockages by developing a rule-based warehouse planning model in ErgoAI that optimized robot task sequences and path efficiency.
- Decreased error rates by 25% during high-density operations by encoding 30+ robotic workflow algorithms with collision avoidance and movement restrictions.
- Improved throughput by 40% and reduced debugging time by 30% by integrating ML-based prediction for task routing and dynamic allocation into a plan verification pipeline.
Software Engineer Intern
HCL Technologies Limited, Chennai, India | Nov 2023 - Mar 2024
- Engineered and deployed a movie recommendation web application using Python, GitOps, CI/CD pipelines, and DevOps automation, improving deployment speed by 40%.
- Automated infrastructure provisioning using Git, Jenkins, Docker, Kubernetes, and Terraform, reducing manual errors by 75% and cutting deployment downtime by 30%.
- Designed NLP and ML pipelines for sentiment analysis and feature extraction, enhancing data processing efficiency by 30%.
- Deployed applications on Azure and AWS with Prometheus and Datadog monitoring, streamlining real-time engagement by 50%.