ML Engineer with 1+ years in Deep Learning Models & Inference Pipelines
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ML Engineer with hands-on experience fine-tuning deep learning models, building end-to-end inference pipelines, and writing evaluation frameworks. Proficient in PyTorch, Hugging Face (transformers, PEFT, datasets), and FastAPI. Built and deployed production-grade computer vision and NLP systems achieving 97%+ accuracy at ISRO and NIELIT. Experienced integrating LLM APIs (OpenAI, Anthropic, Gemini) and RAG workflows. Comfortable in Linux environments; accustomed to reading ML papers and translating key ideas into working code.
Jawaharlal Nehru Engineering College
B.Tech · Electronics and Computer Engineering
August 1, 2021 – June 30, 2025
PHN Technology
Technology Educator & Operational Executive
July 1, 2025 – Present
Pune, Maharashtra, India
NIELIT
Research Intern - AI/ML Pipeline Development
February 1, 2025 – June 1, 2025
Aurangabad, Maharashtra, India
ISRO – LPSC
Research Intern - Computer Vision & Deep Learning
December 1, 2024 – January 1, 2025
Thiruvananthapuram, Kerala, India
Detection of Diabetic Retinopathy
June 24, 2026 – Present
Built an automated medical image analysis pipeline: contrast enhancement → noise filtering → CNN feature learning (AlexNet) → severity classification on EyePACS dataset (~73,000 retinal images), achieving ~97.6% accuracy. Implemented end-to-end pipeline in Python using TensorFlow/Keras, OpenCV, and NumPy; managed full model training loop and evaluation benchmarks.
Real-Time Dynamic Sign Language Recognition (LSTM)
June 24, 2026 – Present
Wrote custom training loops from scratch using PyTorch; implemented bidirectional LSTM with 45-frame temporal sequences for ISL gesture classification — achieving 100% test accuracy and ~98% generalisation accuracy. Designed the full inference pipeline: video preprocessing → keypoint extraction → sequence modelling → live webcam output, demonstrating applicability in assistive HCI. Built end-to-end computer vision inference pipeline: real-time webcam input → preprocessing → model inference → output, using Python, TensorFlow, OpenCV, and NumPy. Conducted comparative study of Transformer-based 3D CNNs and Graph Neural Networks (GNNs); implemented experimental prototypes and evaluated performance trade-offs by reading and implementing key ideas from ML papers.
Solar-Based Entertainment Satellite - KAJWA
Indian Patent Application
January 1, 2022 – Present
Autonomous 3D Printing Rover for Space Construction
German Patent Application
January 1, 2020 – Present
Cultural Fit Analysis
The candidate exhibits a strong cultural fit for an ML Engineer role, particularly in an innovative and research-oriented environment. Their diverse project portfolio, including medical image analysis, real-time sign language recognition, and autonomous systems for space, showcases a broad interest and ability to apply ML to various domains. Involvement in GDSC and TEDx demonstrates leadership, community engagement, and a proactive attitude, which are valuable for team collaboration and knowledge sharing. The focus on end-to-end pipeline development and performance optimization aligns well with industry best practices.
Soft Skills & Operational Fit
The candidate demonstrates strong communication skills through clear project descriptions and a well-structured resume. Their experience as a Technology Educator and involvement in GDSC indicate leadership, mentorship, and collaboration abilities. The ability to design automated curriculum delivery and reporting pipelines suggests strong organizational and operational skills, akin to MLOps pipeline design. The candidate's comfort with Linux environments and reading ML papers points to a proactive and self-sufficient learning approach.