Team Lead, Machine Learning Platform

I am currently an Engineering Manager at and my expertise is in machine learning and natural language processing. You can find my full cv here.

My mission is to create software and tools that help business leverage machine learning to bring next generation products and services to the market.

Skills: machine learning, natural language processing, python, tensorflow, pytorch

Practical Machine Learning with Rust


Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud.

After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy.

Book website Github repository with all code Buy on Amazon

fastText Quick Start Guide


Facebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and efficient data insights requires powerful NLP tools like fastText.

This book is your ideal introduction to fastText. You will learn to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification.

Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow and PyTorch.

Finally, you will deploy the fastText models to mobile. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or projects.

Book website Github repository with all code Buy on Amazon

Professional Experience

role: Engineering Manager - NLP

Jan 2021 → current

  • Design and implement next generation AI/NLP infrastructure for building and deploying thousands of models at scale in conversational AI.
  • Responsible for research, development, production, and scaling of full pipeline of message flow and other intelligent NLP systems in multi‐lingual and multi‐modal contexts.
  • Research, implementation, benchmark existing literature for various purposes.
  • Leading the team which works in parallel and in collaboration to produce custom‐made algorithms using machine learning/Deep learning frameworks‐libraries like torch/hugging face/TensorFlow.
  • Prepare production‐ready code with pre/postprocessing and native scaling in Kubernetes or on‐prem environment.
  • Responsible for all MLOps, Cloud infra management for ML‐related service.
  • As a lead, I am answerable for client issues ﴾ explanation on models prediction and performance﴿, custom requirements, new product feature exploration, POCs, architecture and design discussion, problem solving, hiring, mentorship and other decision making tasks.

Company Website


role: Team Lead - Machine Learning Platform

May 2017 → Dec 2020

As an individual contributor.

  • Develop, Optimize and improve existing data pipelines to support our growth, initiatives around performance and scalability.
  • Thoroughly understand software requirements, deliverables and timelines.
  • Unit and system test new services, releases and software upgrades.
  • Develop initiatives on ensuring data quality, accuracy and reliability.
  • Researching, prototyping and developing machine learning and statistical models to more accurately forecast, mine and segment data.
  • Researching, prototyping and developing machine learning and statistical models to more accurately forecast, mine and segment data.
  • Evaluate emerging datasets and technologies that may contribute to our analytical platform.

As a technical lead.

  • Ensure that all software developed within the team satisfies the business requirements as specified.
  • Participate and lead discussion in sprint plannings, design reviews and all team related matters.
  • Assume a high level of ownership of all software developed by the members of the team.
  • Actively contribute to the process of continual improvement, with regard to self, team members and process using various methods such as effective retrospections, discussions and one-on-ones.

Company Website


role: Category Head - Python

Nov 2016 → May 2017

  • Planning and Requirement Analysis
  • Development of python practise section for B2C engagement.
  • Coding, Prototyping and testing applications.
  • Creating software for high user engagement.

Company Website

SLK Software Services Pvt Ltd.

role: Software Engineer

Aug 2015 → Oct 2016

  • Design, develop and implement applications appropriate to business needs and requirements.
  • Interface with customers to identify and understand their business goals.
  • Troubleshooting and error detection in various applications.
  • Provide strategic organizational direction in developing applications.
  • Identify and define plans, methodologies and deliverables for assigned projects and releases.
  • Develop system documentation protocols.

Company website

Tata Consultancy Services.

role: Systems Engineer

Dec 2011 → Jul 2015

  • Developed various custom automation tools for various tasks.
  • Configuration and other administration of J2EE applications
  • Researching technology options for customer needs.
  • Provided good documentation for use by other team members.
  • Troubleshooting and error detection in various applications.

Company website