Keerthi T

About Keerthi T

Software Engineer with 5 years of experience building scalable, high-performance applications across automotive, IoT, and enterprise environments. I specialize in backend engineering, cloud-native development, and real-time system design using Python, Node.js, React, AWS, and Azure.At Tesla, I design and deploy distributed microservices, high-speed telemetry systems, and predictive analytics pipelines that power battery intelligence and vehicle fleet operations. I bring deep experience in event-driven architectures, REST APIs, microservices, machine learning deployment (Prophet, LSTM), and large-scale data processing.I’m passionate about solving complex engineering problems, improving system reliability, and delivering production-grade software with measurable impact. My strengths include DevOps automation (Jenkins, Docker, Kubernetes), cloud engineering, and building interactive dashboards and real-time interfaces.I’m actively looking for Software Engineer / Backend Engineer / Full Stack roles where I can contribute to scalable systems, cloud platforms, and high-impact products

Experience

1. Software Engineer

   Tesla

   Apr 2024 — Present · TX, US

   Designed and deployed scalable backend microservices using Node.js and Python (FastAPI) to process battery data and vehicle telemetry, achieving a 40% reduction in API latency through Kubernetes orchestration and AWS Lambda integration.•Developed an interactive battery performance dashboard with React, Redux, and D3.js, hosted on AWS Amplify, leading to a 25% increase in user engagement.•Built time-series forecasting models with Prophet and LSTM for predictive maintenance, resulting in an 18% decrease in vehicle service center visits; models deployed using AWS IoT Core and visualized through Grafana.•Engineered a real-time vehicle status monitoring interface using React.js, WebSockets, React Hooks, and Material-UI, achieving sub-100ms latency for live telemetry data and enhancing fleet operations efficiency and system responsiveness.•Optimized complex PostgreSQL queries with advanced indexing and performance tuning, yielding a 35% improvement in database efficiency across analytics and diagnostics systems.•Developed and deployed a machine learning-based intrusion detection system (IDS) for CAN bus protocols using Zeek and the Elastic Stack, identifying and mitigating over 70 monthly intrusion attempts.•Utilized advanced prompt engineering techniques with internal LLMs to rapidly prototype solutions for complex control algorithms and anomaly detection rules, significantly accelerating development cycles for vehicle firmware updates and the CAN bus IDS.

Education

• Jawaharlal Nehru Technological University Hyderabad (JNTUH)

  Bachelor of Technology - BTech, electronics and communications engineering

• George Mason University

  Master's degree, Cloud based Information Systems