Tanseer Saji

A Machine Learning Engineer with practical expertise in Python and Pytorch ML framework, possessing hands-on experience with backend frameworks such as Django, Flask, and FastAPI. Experienced in utilizing React.js and Three.js for developing user interfaces. Has a comprehensive understanding of Agile Scrum methodology and has worked in such an environment. Proficient in all aspects of backend engineering, including development, testing, and DevOps utilizing Nginx, Docker, and Kubernetes. Familiar with AWS RDS, EKS, as well as Artificial Intelligence and Blockchain technologies.


Venture Highway LLP

Backend and Automation Lead

Bangalore, India (March 2022 - Present)

  • Developed a set of automation tools for startup and founder discovery, resulting in a 50% reduction in manual tasks, improved efficiency, and accelerated the process of identifying potential founders and startups.
  • Developed and implemented a pipeline that accurately calculates the probability of individuals becoming successful founders, resulting in a 25% increase in accurate predictions compared to previous methods.
  • Reduced processing time by 67%
  • Implemented AI tools that assessed the ROI and relevancy of a startup, resulting in a 20% increase in profitability and a 30% reduction in irrelevant processes.

DNF Labs

Machine Learning Engineer

Dubai, UAE (Jul 2020 - Mar 2022)

  • Worked with various clients that has National Security importance
  • Developed a AI powered platform for Lenovo to generate unique Artworks from user's images
  • Developed a Digital Twin for Emirates Engine Maintenance Center to carry out the entrire Engine service process from start to Finish along with tracking capability
  • Created a quotation management system, that allows upper management to share project quotes with client

Floss Creatives

Machine Learning Engineer

Dubai, UAE (Jan 2020 to Jul 2020)

New Delhi, India (Mar 2020 to Dec 2020)

  • Created a Crowd Tracking System to analyse the movement of attendees at an event
  • Researched and Developed an IoT device that predicts the Water Quality Index
  • Designed and Developed the Company Website
  • Developed a Psychometric Analysis system that provided an indepth report about a candidate's personality
  • Provided AI assistance to Advertising team


Unsupervised Sentiment Analysis using small recurrent language models

Vivekananda Journal of Research

Published on: Dec 2019

We explore the possibility of unsupervised byte-level sentiment learning of a sentence in the English language using small recurrent language models. Long Short-Term Memory (LSTM) network is a simple and effective network to use while working with sequential data like text or audio. As LSTM processes the data it learns all the information regarding the given input in the context of all the inputs before that. A. Radford et al [1] provided the evidence that a multiplicative LSTM (mLSTM) [9] is able to learn the concept of sentiment in a manipulable way, but they were able to achieve this result due to the huge amount of data samples used for training. This paper tries to investigate the neuron or neurons responsible for sentiment analysis inside a Long Short-Term Memory (LSTM) network when there is a limited amount of training samples available.

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Predicting Biological Oxygen Demand and pH of Water with Multiple Regression Methods

Journal of Earth and Envionmental Sciences

Published on: Jul 2019

This paper compares the learning curve generated by different machine learning models by predicting Biological Oxygen Demand (BOD) and pH given Chemical Oxygen Demand in the water sample. The continuing advancements in the field of sensor interface development are to calibrate and correct the inherent non-idealities present in transducers forced us to work in this area. Machine Learning algorithms have made a profound impact in the field of Science and Engineering in the past few decades. The purpose of this paper is to propose an approach which is more users friendly and fast in operation by modelling and optimization of sensor used for dissolved oxygen measurement. This is to overcome several drawbacks generally found in the previous work like complex designing, nonlinearity and long computation time. It is found that there is a possibility to replace hardware sensor technology by Machine Learning and Artificial Intelligence provided appropriate and sufficient data.

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Masters in Computer Applications

Amrita Vishwavidyapeetham

Coimbatore, India

Graduation on 2024
Major Field: Artificial Intelligence Minor Field: Mathematics

Bachelor of Computer Applications

Guru Gobind Singh Indraprastha University

New Delhi, India

Graduated on 2019
Major Field: Computer Applications
Minor Field: Mathematics

12th Grade

Kalka Public School

New Delhi, India

Graduated on 2016
CBSE - PCM with Computer Science