Open to opportunities

Ashish Sharma

@ashishsharma9

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B.Tech AI/ML engineer building real-time vision and acoustic systems.

India

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What I'm looking for

I seek an entry-level ML/AI role focusing on real-time vision or acoustic systems, edge deployment, and growth in production ML engineering within collaborative, impact-driven teams.

I am a B.Tech student in Electronics and Instrumentation Engineering with hands-on experience building real-time AI systems for challenging environments.

I've developed fog-aware, GPU-accelerated vision classifiers and CNN-based acoustic models that delivered high accuracy and low latency for edge and real-time applications.

My internships involved end-to-end ML pipelines: automated data ingestion, preprocessing, model training, quantization, cloud deployment, and performance tuning for smart-grid and defense use cases.

I combine strong technical skills in Python, PyTorch, Scikit-learn and cloud deployment with practical experience in signal processing, computer vision, and system optimization to deliver production-ready solutions.

Experience

Work history, roles, and key accomplishments

Underwater AI Researcher

NIT Rourkela (Student Project)

May 2025 - Dec 2025 (7 months)

Designed a CNN-based acoustic recognition system classifying 8 ship types at 91% accuracy and estimating distance with 3% MAE using spectrograms and VMD-enhanced multi-domain features for real-time sonar processing.

Python CNN Signal Processing Spectrograms VMD

Summer Intern - AI Vision

Integrated Testing Range (ITR), DRDO

May 2025 - Jun 2025 (1 month)

Built a real-time fog-aware AI vision system using CLAHE and adaptive preprocessing with 89.7% accuracy and GPU-accelerated SVM classification reducing latency to 40ms for edge deployment.

Python Computer Vision SVM GPU Acceleration CLAHE Model Quantization

Summer Intern - ML

Virtual and Intelligent Instrumentation Laboratory, NIT Rour

May 2024 - Jun 2024 (1 month)

Developed a hybrid ML pipeline (XGBoost, LightGBM) for wind forecasting that reduced prediction error by 18% and improved smart-grid scheduling precision by 22%, deployed for real-time inference on Google Cloud.

Python XGBoost LightGBM Deploying Data Ingestion

Education

Degrees, certifications, and relevant coursework

National Institute of Technology, Rourkela

Bachelor of Technology, Electronics and Instrumentation Engineering

2022 -

Grade: CGPA - 7.34

Activities and societies: Men’s Hockey Team member, NIT Rourkela (2023–Present)

B.Tech. in Electronics and Instrumentation Engineering ongoing at NIT Rourkela with coursework in deep learning, computer vision, and cloud computing; current CGPA 7.34.