Abu Talha Ansari
@abutalhaansari
AI/ML engineer focused on deep learning, NLP, and production-ready model deployment.
What I'm looking for
I am a third-year Artificial Intelligence and Machine Learning student with hands-on experience building end-to-end AI solutions, from data preprocessing to production-ready model deployment.
I have implemented and evaluated multiple deep learning architectures (LSTM, Bi-LSTM, GRU, CNN-LSTM, Attention-based models) for real-world NLP tasks, achieving over 90% accuracy on e-commerce text classification and delivering robust, optimized pipelines.
Through internships and project leadership, I have developed semantic search systems, anomaly detection for smart grids, and AI-enhanced retail prototypes, often integrating APIs, vector search (FAISS), and cloud tools to create explainable, user-centric products.
I value collaboration, continual learning, and ethical AI practices, and I seek roles where I can contribute technical depth, creativity, and problem-solving to innovative teams pushing the boundaries of AI.
Experience
Work history, roles, and key accomplishments
Summer Intern
AIQuantum Smart Solutions Private Limited
Apr 2025 - May 2025 (1 month)
Designed and implemented 5+ deep learning architectures for e-commerce text classification, achieving over 90% accuracy and improving category tagging reliability; developed production-ready preprocessing and ML pipelines and led comparative evaluation to select attention-based LSTM for best performance.
Education
Degrees, certifications, and relevant coursework
New Horizon College of Engineering
Bachelor of Engineering, Artificial Intelligence & Machine Learning
2023 -
Grade: 9.71/10.0
Activities and societies: Project work in deep learning, NLP, semantic search, and internship experience; technical clubs and hackathons.
Pursuing a Bachelor of Engineering in Artificial Intelligence & Machine Learning with strong academic performance (CGPA: 9.71/10.0) and hands-on projects in deep learning and NLP.
Cambridge PU College
Pre-University Course, Pre-University Course (PCMC)
2021 - 2023
Grade: 94.16%
Completed Pre-University Course (PCMC) with a focus on Physics, Chemistry, Mathematics, and Computer Science, achieving 94.16% overall.
SJES Central School
Secondary School Leaving Certificate (CBSE), Secondary Education
Grade: 86.5%
Completed Secondary School Leaving Certificate (CBSE) with a percentage of 86.5%.
Availability
Location
Authorized to work in
Website
gamerbhai02.netlify.appJob categories
Skills
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