Piyush Tyagi
@piyushtyagi
Machine Learning Engineer and Full Stack Developer deploying scalable AI/ML systems.
What I'm looking for
I’m a Machine Learning Engineer and Full Stack Software Developer focused on deploying AI/ML models and optimizing end-to-end workflows. I enjoy turning complex ideas into scalable software that delivers measurable impact.
In my recent AI/ML internship, I built a CV + UWB warehouse inventory locator, cutting reel retrieval time by 65% across 1,000+ assets, and developed a real-time CV safety compliance system that reduced floor violations by 60% through automated flagging. Earlier, I created an M2 forecasting suite for 100+ indicators, reaching 85% prediction accuracy, and improved predictive-data usage by 40% by streamlining deployment for instant access.
I’ve also built computer-vision and automation solutions, including YOLO/OpenCV AOI defect detection (95% accuracy) and OCR-to-TTS workflows that reduced processing time by 30% across 50+ test cases. I like working end-to-end—models, APIs, and user-facing interfaces—using tools like Python, ReactJS, FastAPI/Flask, and SQL-backed systems.
Beyond engineering, I lead and mentor—directing a 30+ team hackathon with 600+ participants and mentoring 100+ students. I’m driven by results, clear communication, and building products that help people move faster with confidence.
Experience
Work history, roles, and key accomplishments
AI/ML Developer Intern
Autonexai360
Jan 2026 - May 2026 (4 months)
Built a CV + UWB-based warehouse inventory locator that cut reel retrieval time by 65% across 1,000+ assets. Developed a real-time CV safety compliance system, reducing floor violations by 60% via automated flagging.
Tech Intern
Shilaz Tech
Jul 2025 - Sep 2025 (2 months)
Built an M2 forecasting suite for 100+ G20 indicators, achieving 85% prediction accuracy with ML algorithms. Increased predictive-data usage by 40% by streamlining deployment for instant access.
Siemens Intern
Siemens (SRAPL)
Dec 2024 - Jan 2025 (1 month)
Developed AOI defect-detection models using YOLO and OpenCV, achieving 95% accuracy and 60% faster inspection. Reduced user input time by 50% and manual paperwork by 80% by automating documentation workflows.
Research Intern
KJSSE
May 2024 - Sep 2024 (4 months)
Improved AI assistive systems, reaching 92% object-detection accuracy and increasing TTS efficiency by 30%. Optimized facial recognition throughput by 40% and processed 5,000+ datasets for OCR and TTS innovation.
Education
Degrees, certifications, and relevant coursework
K.J Somaiya College of Engineering
Bachelor of Electronics and Computer Science, Electronics and Computer Science
2022 -
Grade: CGPA 9.057
Pursuing a Bachelor of Electronics and Computer Science degree. Current CGPA is 9.057.
Availability
Location
Authorized to work in
Job categories
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