Meghana Bhange, Developer in Montreal, QC, Canada

Meghana Bhange

Software Developer

Montreal, QC, Canada
Toptal Member Since
October 27, 2022

Meghana is a machine learning engineer with a passion for solving problems in a data-driven manner. She has experience in natural language processing and has previously published at a conference and given talks on custom entity detection at the PyData meetup in Bangalore. Meghana is passionate about working on creative projects and always looks for new ways to apply her skills.

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TISL Lab at ETS Montreal
Python 3, Artificial Intelligence (AI), Scikit-learn (via Toptal)
Artificial Intelligence (AI), ChatGPT, OpenAI, Next.js, FastAPI, DigitalOcean...
Python 3, Machine Learning, SQL, Artificial Intelligence (AI), Deep Learning...


Machine Learning - 2 yearsKubernetes - 2 yearsPython - 2 yearsNatural Language Processing (NLP) - 2 yearsGPT - 2 yearsAmazon SageMaker - 1 yearData Engineering - 1 yearDjango - 1 year


Montreal, QC, Canada



Preferred Environment

Visual Studio Code (VS Code), Slack, Python, Generative Pre-trained Transformers (GPT), GPT, Natural Language Processing (NLP)

The most amazing...

...project I've worked on is developing an end-to-end custom recognition service on resource-constrained code-mixed settings with low latency requirements.

Work Experience

2022 - PRESENT

Affiliated Researcher

TISL Lab at ETS Montreal
  • Researched model extraction attacks on machine learning systems with counterfactual explanation APIs.
  • Modeled an adversary that can leverage the information provided by counterfactual explanations to build high-fidelity and high-accuracy model extraction attacks.
  • Benchmarked the model performance on the Folktables dataset, with the extracted model gaining fidelity of around 97.6%.
Technologies: Python 3, Artificial Intelligence (AI), Scikit-learn
2023 - 2023

OpenAI Developer (via Toptal)
  • Built a FastAPI back end with GPT-3 API integration to generate a travel itinerary for a trip and extract locations. These locations were then geo-encoded with co-ordinates.
  • Built a Next.js app to display the travel itinerary and show the geo-locations on Google Maps color-codes corresponding to days in the trip and information about each location.
  • Dockerized and deployed both the FastAPI back end and Next.js front end to DigitalOcean.
Technologies: Artificial Intelligence (AI), ChatGPT, OpenAI, Next.js, FastAPI, DigitalOcean, Large Language Model (LLM), Generative Pre-trained Transformers (GPT), GPT, Natural Language Processing (NLP), Web Development
2021 - 2022

Machine Learning Engineer
  • Researched and built analytical tools for evaluating threat-hunting detectors and understanding abnormal patterns in detection outputs.
  • Organized the monitoring and quality check infrastructure in machine learning detectors.
  • Created a framework for deep investigation of threats.
Technologies: Python 3, Machine Learning, SQL, Artificial Intelligence (AI), Deep Learning, Scikit-learn, AI Design, Machine Learning Operations (MLOps), Amazon SageMaker, Kubernetes, APIs, Data Pipelines, Amazon Web Services (AWS), PostgreSQL, Data Engineering
2020 - 2021

Machine Learning Engineer

  • Contributed to the intent recognition service using a sentence transformer to improve the top-K recall and accuracy, which improved F1 by 40% absolute.
  • Designed, built, and deployed a multi-lingual name recognition service across all clients.
  • Evaluated the performance of various language models like ULMFiT and VAMPIRE for low-resource language contexts.
  • Created synthetic training data for FAQ systems in a chatbot using Generative Pre-trained Transformer 3 (GPT3) AI.
Technologies: Machine Learning, Python 3, Pandas, SpaCy, Chatbots, OpenAI, Artificial Intelligence (AI), Deep Learning, Scikit-learn, AI Design, Django, Google Cloud, Google Cloud Platform (GCP), Speech Recognition, APIs, Web Development, Text Generation, Language Models, Large Language Model (LLM), Flask, Data Pipelines, Machine Learning Operations (MLOps), Computational Linguistics, DaVinci, Generative Pre-trained Transformers (GPT), PostgreSQL
2019 - 2019

Machine Learning Intern

  • Created a person-name extractor customized for multilingual conversations. Tweaked Flair, Facebook's natural language processing library, to work on low-latency use cases in English, Spanish, and French.
  • Improved the final model achieves by 47% in F1 compared to the previously deployed FastText mode.
  • Deployed the developed multilingual name extractor to production with overall latency of under 500 milliseconds.
Technologies: Artificial Intelligence (AI), Chatbots, Deep Learning, Scikit-learn, Text Generation, Language Models, Large Language Model (LLM), Flask, PostgreSQL


Model Extraction Attack Using Counterfactual Explanation

A research project that I worked on with the Trustworthy Information Systems Lab at ETS Montreal. We researched how model adversaries can leverage the information provided by counterfactual explanations to build high-fidelity and high-accuracy model extraction attacks.

LitNER | Literature Named Entity Recognition
Named entity recognition based on Spacy3 trained on the LitBank dataset. This project uses Roberta XLM as a base model and fine-tuned literature data to understand the terms generally used in literature. The pre-trained model released with the project can also be used to perform NER tasks on any literary text.

Hinglish Twitter Sentiment Detection | SemEval2020
This work adds two common approaches, fine-tuning large transformer models and sample efficient methods like ULMFiT. Prior work demonstrated the efficacy of classical ML methods for polarity detection. We fine-tuned general-purpose languages representation models, such as those of the BERT family, which are benchmarked along with classical machine learning and ensemble methods. We showed that NB-SVM beats RoBERTa by 6.2%. The best-performing model is a majority-vote ensemble which achieves an F1 of 0.707.

Wikipedia Textbook Assistant
Simple streamlit application to be paired with textbooks so that one can easily extract the keyphrases from the text they are reading and get detailed information from Wikipedia to understand the relevant context.

Artificial Insanity (Cards Against Humanity with Stable Diffusion) | Toptal Hackathon

Artificial Insanity is a multiplayer game for the Toptal Hackathon, which uses Stable Diffusion to generate image responses for a text prompt. In each round, a player chooses prompt, and other players generate a response using an integrated Stable Diffusion interface.

I benchmarked performance in terms of quality and latency for DALLE and Stable Diffusion. Also, I deployed the final model on FastAPI to make it easier to integrate with the rest of the back end. The solution won the second prize in the Hackathon.



Python, SQL, Python 3


Machine Learning, Natural Language Processing (NLP), Artificial Intelligence (AI), Deep Learning, APIs, Text Generation, Language Models, GPT, Data Engineering, Chatbots, OpenAI, AI Design, Machine Learning Operations (MLOps), Large Language Model (LLM), Computational Linguistics, Generative Pre-trained Transformers (GPT), Research, Streamlit, Transfer Learning, BERT, Signals, Information Theory, Custom BERT, Stable Diffusion, DALL-E, FastAPI, Inference API, Speech Recognition, Web Development, DaVinci, Systems, ChatGPT


Django, Flask, Next.js


Pandas, Scikit-learn, SpaCy


Data Pipelines, PostgreSQL, Google Cloud


Slack, Named-entity Recognition (NER), Amazon SageMaker


Kubernetes, Google Cloud Platform (GCP), Amazon Web Services (AWS), Visual Studio Code (VS Code), DigitalOcean

Industry Expertise



2016 - 2020

Bachelor's Degree in Electronics and Telecommunication Engineering

Savitribai Phule Pune University - Pune, India