Shayan Sadar
Early Stage Product Design And Research at Stealth
India
Summary
👤
Senior🎓
Top SchoolShayan Sadar is an engineer and researcher with 8 years’ experience building ML-driven backend systems, currently focused on early-stage product design and research. He combines practical backend skills (Python, Node.js, SQL, MongoDB, Redis) with applied ML and NLP expertise (PyTorch, spaCy, OpenCV) to turn prototypes into production-ready services. Previously he led AI solutions and senior engineering efforts at QliqSOFT, scaling ML workflows and system architecture across real-world deployments. Shayan holds a BE in Computer Engineering and an MS in Computer Science from UT Dallas, blending strong academic training with hands-on product delivery. Based in India, he’s open to full-time roles where he can bridge research, system design, and backend engineering to deliver measurable product impact.
8 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Computer Science, 3.6, Master of Science - MS, Computer Science, 3.6 at The University of Texas at DallasISC, Science, 89.25%, ISC, Science, 89.25% at St. Mary's Junior College, PuneBachelor of Engineering (BE), Computer Engineering, First Class With Distinction, Bachelor of Engineering (BE), Computer Engineering, First Class With Distinction at Savitribai Phule Pune UniversityICSE, 91.6%, ICSE, 91.6% at St. Mary's School, Pune
Urdu, Marathi, Hindi, English
Stackoverflow
2,261reputation
257kreached
23answers
4questions
virtualenv
top-5%
python
top-5%
Github Skills (150)
python10
bots9
gpu9
deep-learning9
machine-learning9
bioinformatics9
conversations9
google-assistant9
virtualenv9
tensorflow9
nlp8
dialogflow8
database8
chatbot8
voice8
Programming languages (6)
Github contributions (5)
shayan09/ngam-text-generation
Nov 2019 - Apr 2024
Generating text using N-gram models that were trained on shakespeare.txt and warpeace.txt. n-gram models are widely used in statistical natural language processing. In speech recognition, phonemes and sequences of phonemes are modeled using a n-gram distribution.
Contributions:5 pushes, 1 branch in 4 years 6 months
language-processingspeech-recognitionphonemesshakespearetopic-modeling
shayan09/Hands-on-Machine-Learning-Using-Python
Jan 2019 - Apr 2024
Contributions:14 pushes in 5 years 4 months
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