About Me
AI/ML Embedded Software Engineer specializing in the intersection of deep learning and hardware-aware inference. Proven experience building end-to-end MLOps pipelines, deploying models across NVIDIA Jetson, Hailo-8, and AMD Versal, and optimizing computer vision systems for real-world edge environments. Passionate about transforming heavy ML architectures into high-throughput, low-latency production deployments.
Skills & Technologies
Work Experience
AI / ML Embedded Software Engineer
Engineered automated conversion pipelines for PyTorch, TensorFlow, and ONNX models to optimized deployment formats including TensorRT and Hailo HEF, optimizing throughput and hardware efficiency. Built a model inspection platform and automated layer-testing agents to reduce retraining and speed feedback cycles. Architected an internal dataset intelligence platform using FastAPI, React, and Vite, improving data scientist productivity, recognized with 3rd place in company hackathon. Established MLOps infrastructure with Docker/Podman standards and integrated MLflow, ClearML, and model registries with documentation.
AI & Computer Vision Intern
Delivered 7 end-to-end ML projects including plant disease detection with 500+ classes and speech emotion recognition achieving 85% accuracy. Managed dataset preparation, model training, optimization, and deployment pipelines.
Projects
Edge-AI Enabled UAS for Search and Rescue (SAR)
Designed a two-drone swarm using Jetson Orin companion computers and Pixhawk controllers communicating via MAVLink/UART. Developed a custom Python GNC stack with dynamic path planning and Offboard Control. Deployed YOLOv8 for real-time human detection at 30 FPS with GPS geotagging and built a resilient WiFi/4G telemetry bridge for BVLOS communication.
Personalized AI Shadow – LLM Fine-Tuning
Built a personalized LLM assistant replicating tone and reasoning style. Curated a large 3.4M entry technical and reasoning dataset (1.9GB). Fine-tuned the Mistral-7B model over 137+ hours achieving a loss of 0.35. Merged and quantized the model to GGUF format for efficient offline CPU deployment.
VisionNet – Edge AI Drone Platform
Developed an AI drone system for crack detection and solar panel inspection achieving 99.3% accuracy with 250 ms median latency. Applied TensorRT and INT8 quantization for real-time edge inference on Jetson Nano and Raspberry Pi hardware.
Education
B.Tech
Computer Science (AI/ML)