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TejasweeSulekh/README.md

Hi there, I'm Tejaswee Sulekh!

AI & Systems Engineer | Edge ML, Low-Latency Backends & Optimization

LinkedIn Portfolio


About Me

I am an AI and Systems Engineer with a background in Electrical Engineering from IIT Bombay. My work sits at the intersection of hardware and software, focusing from how complex machine learning models can be adapted to run efficiently at scale and on the edge to how to extract as much performance from a model without loosing out on any energy or memory constraint.

I am passionate about building fast, system-aware software. If a challenge involves improving throughput, squeezing out latency, or fine-tuning an architecture for peak efficiency, I am fully engaged. Having a firm grasp of the underlying hardware allows me to write highly optimized code, whether I am deploying AI to resource-constrained devices or managing complex, high-concurrency infrastructure.

  • Focus Architecting low-latency, event-driven backends and building lightweight, locally hosted AI ecosystems.
  • Deep Dive Hardware-level software optimization, zero-bloat web frameworks, and resilient end-to-end MLOps lifecycles.
  • Ask Me About Algorithmic design, rigorous memory management, and bridging the gap between high-level APIs and hardware execution.

Tech Stack & Tools

Category Technologies
Languages Python C++ C JavaScript
Machine Learning TensorFlow Keras MLflow
DevOps & Data Docker Kafka Git Linux FastAPI

Featured Projects

  • Real-Time Fraud Detection System Architected and deployed a scalable pipeline capable of detecting fraudulent activity in real-time data streams. (Technologies: Python, Kafka, Docker, FastAPI, MLflow, Streamlit)

  • Edge-Deployable AI Agent In the process of building a locally hosted AI assistant utilizing modern software principles to ensure user privacy, low latency, and efficient resource usage. (Technologies: Ollama, FastAPI, Streamlit)

  • Audio Signal Processing Framework Developed and optimized highly efficient low-latency pipelines for complex audio processing and its analysis. (Technologies: Python, TensorFlow, C)

  • IoT Neural Network Integration Programmed a hardware abstraction layer (HAL) and implemented OTA updates to bridge software neural networks with physical Bluetooth Low Energy (BLE) edge devices. (Technologies: C/C++, Embedded Systems)


GitHub Stats

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  1. TejasweeSulekh TejasweeSulekh Public

    Introduction about myself

    1

  2. keras-core keras-core Public

    Forked from keras-team/keras-core

    A multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.

    Python 1

  3. Microprocessors Microprocessors Public

    Microprocessors Lab - Spring Semester 2022 - Indian Institute of Technology, Bombay

    Assembly 1

  4. FPGA-Handwritten-Digit-Classification FPGA-Handwritten-Digit-Classification Public

    Embedded Systems - Spring Semester 2022 - Indian Institute of Technology, Bombay

    Python 1

  5. Digital-Signal-Processing Digital-Signal-Processing Public

    Digital Signal Processing - Spring Semester 2022 - Indian Institute of Technology, Bombay

    Jupyter Notebook 1

  6. Programmable-Power-Supply Programmable-Power-Supply Public

    Electronic Design Lab - Spring Semester 2022 - Indian Institute of Technology

    C 1