Nabeel Qaiser

CTO

Bengaluru, Karnataka, India6 yrs 6 mos experience

Highly StableAI Enabled

Key Highlights

6+ years of experience in backend development.
Ranked under 500 in pan-India CodeX event.
Expertise in Deep Learning and NLP frameworks.

Stackforce AI infers this person is a Backend Developer with expertise in SaaS and advanced NLP technologies.

Contact

nabeel.qaiser.sde@gmail.com LinkedIn

Skills

Core Skills

Backend DevelopmentDatabase ManagementNatural Language ProcessingMachine Learning

Other Skills

ALBERTAlgorithmsArtificial Intelligence (AI)Artificial Neural NetworksBERTC (Programming Language)C programmingData StructuresDeep LearningFlaskGo (Programming Language)Graph Neural NetworksJavaScriptMongoDBNatural Language Processing (NLP)

About

Computer Science professional with 6+ years of industry experience. Currently working as Senior Member of Technical Staff at Salesforce. Built Fishgram Aqua ( https://aqua.captainfresh.com ) at Captain Fresh to digitize the Aquaculture practices. While also owned and maintained the development of multiple backend services during the employment. Demonstrated problem-solving skills in algorithms and data structures. Secured a rank under 500 pan-India in the CodeX event organized by Scaler Academy. Have worked with various state of the art models, methods, and frameworks in Deep Learning and NLP such as BERT, PyTorch, RASA, Artificial Neural Networks, RNNs, LSTMs, seq2seq, Attention, Transformers, Word Embedding, node2vec, etc. Seeks opportunities to apply domain knowledge to deliver business solutions efficiently and at scale.

Experience

Salesforce

Senior Member of Technical Staff

Aug 2025 – Present · 7 mos · Hybrid

Captain fresh

Backend Engineer

Jul 2022 – Aug 2025 · 3 yrs 1 mo · Bengaluru, Karnataka, India

Getmega

Back End Developer

Aug 2021 – Jun 2022 · 10 mos · Banglore

Tcs research & innovation

Researcher

Aug 2019 – Aug 2021 · 2 yrs · Gurgaon, India

Successfully engineered a chatbot, which can converse with respect to the information/knowledge that has been accumulated from various resources. The prime focus is given to the business news domain.
Extended the RASA framework for building the chatbot.
Introduced the comprehension reader module into the framework to handle unexpected questions.
Reduced memory consumption by 80%. Used the ALBERT architecture over BERT for the comprehension reader.
Automated the hand-picked feature engineering by extending the graph neural networks, a representational learning model for the large scale graphs, on the real-world social media network.