// Edge AI · Industrial IoT · AWS Cloud · Linux Systems
// NVIDIA Jetson · Raspberry Pi · MQTT · LSTM · Transformer
I build intelligent systems that live at the edge — deploying optimised ML models on embedded Linux hardware, engineering low-latency MQTT pipelines, and backing everything with production-grade AWS cloud infrastructure. Currently leading industrial leak detection for live crude oil pipelines at Bharat Flow Analytics.
Hands-on experience building and deploying LSTM, Transformer, and Autoencoder architectures for real-time anomaly detection on live industrial sensor data. Trained models are optimised for low-power edge hardware using TensorFlow and PyTorch, with production deployments on NVIDIA Jetson platforms running embedded Linux. AWS SageMaker and Bedrock experience rounds out the cloud-side ML stack.
Built production IIoT systems for crude oil pipeline monitoring — end-to-end: from UART sensor interfaces and MQTT/TLS pipelines through AWS IoT Core, Lambda, DynamoDB, and S3, to real-time Gunicorn-served dashboards. Implemented Event-Driven Architectures achieving sub-10ms event propagation and OTA firmware updates eliminating the need for manual field visits.
Deep practical Linux experience on NVIDIA Jetson Nano (Jetpack SDK, L4T) and Raspberry Pi — CPU affinity tuning, dynamic frequency scaling, real-time process management, serial communication (UART/AT), and automated system monitoring via shell scripting. Comfortable at the hardware-software boundary: from PCB electrical spec alignment to embedded firmware and kernel-level system optimisation.
I'm a Machine Learning and IoT Engineer (SDE-I) at Bharat Flow Analytics, Pune, where I lead the design, deployment, and client delivery of ML-powered leak detection systems for live crude oil and utility pipeline operations across India.
My work lives at three intersections: embedded Linux systems (NVIDIA Jetson Nano, Raspberry Pi), ML model engineering (LSTM, Transformer, Autoencoder architectures), and cloud-backed IIoT infrastructure (AWS IoT Core, Lambda, DynamoDB, SNS). I've tuned systems at the kernel level — CPU affinity for core-specific task allocation, dynamic frequency scaling, and Linux-based automation scripts for real-time metrics monitoring.
On the Linux side, I'm proficient in system administration, shell scripting, log analysis with grep/regex pipelines, process and performance management, and embedded system configuration on Jetpack SDK. My systems run on field hardware in conditions where reliability is non-negotiable — a context that forces precision.
I mentor engineering interns, coordinate with third-party PCB hardware vendors, and manage the full product lifecycle from technical consultation through field trial. I hold a B.Tech in ECE with an AI/ML minor from MIT World Peace University, and am AWS Certified in Machine Learning.
jetson_clocks — 35% processing speed improvement.ML model design, cloud-integrated dashboards, and client-facing technical delivery for industrial leak detection across the organisation's product portfolio.
Deployed scalable, cloud-connected IoT solutions contributing to a production-ready leak detection system for oil and utility pipelines.
Ongoing hands-on Linux systems study — building production-quality shell utilities for system administration, monitoring, and log management.
I'm actively looking for roles in ML engineering, industrial IoT systems, and embedded Linux engineering — particularly with companies that build products where Linux runs close to the metal: NVIDIA, Qualcomm, NXP, Arm, or engineering-led startups in robotics, autonomous systems, or industrial AI. If you're building something that benefits from both edge hardware depth and cloud architecture experience, let's talk.