I am a Software Engineer with over 4 years of experience designing and building high‑performance, real‑time systems at Jio Platforms Limited. My work focuses on 5G NR Layer 1 (PHY) development, where I have engineered uplink PRACH signal processing pipelines and signal chain modules to achieve ultra‑low latency and improved accuracy. I have deep hands‑on experience in C++, Linux, 3GPP‑compliant telecom systems, and HPC toolchains. Key achievements include -

• Reduced PRACH latency in the Integrated Macro gNodeB Carrier Aggregation (ImgCA) project from 3 ms to 700 µs.
• Delivered a 3 dB gain in Noise & Interference Cancellation (NIC) algorithms for uplink channels.
• Integrated CNN/FNN models into PHY modules to improve preamble detection accuracy.

Alongside telecom work, I have developed scalable backend systems in Node.js (TypeScript) and Java (Spring Boot). I’ve designed and deployed secure RESTful APIs with JWT/OAuth2 authentication, implemented efficient data management with MongoDB and Redis, integrated cloud storage with AWS S3, and built CI/CD pipelines using Docker and GitHub Actions. My work spans enterprise‑grade deployments and startup‑style rapid prototypes, with a consistent focus on performance, scalability, and clean architecture.

I’m passionate about applying artificial intelligence and machine learning to solve real‑world problems. In telecom, I’ve deployed deep learning models for PRACH detection and timing advance estimation. In computer vision, my projects include traffic signal recognition (GTSRB dataset), facial emotion detection, and food image calorie estimation systems. I am actively upskilling in Transformer‑based Large Language Models (encoder, decoder, encoder‑decoder), ResNet/AlexNet architectures, and reinforcement learning—with the goal of building autonomous, agentic AI systems that combine perception, planning, and adaptive decision‑making.