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Srishilesh P S


Srishilesh holds a bachelor's degree in Computer Science & Engineering. He has 2 yrs experience working at Mr.Cooper Group Inc. (Nationstar Mortgage Holdings) as Software Engineer I (Full-stack MLE).

With his passion towards AI & blockchain, he has contributed to several projects & has participated in 20+ hackathons. His aspirations as an entrepreneur gives him a right mix of skills to understand technology & business side of things. Lately, he has been actively learning about machine learning, blockchain, startups & trading.

➡️ Software Engineer 1 @ Mr.Cooper

Full-time . May 2022 - Present

Moved to the ML team from UI team.

⚔️ Key accomplishments
  • Built MLOps pipeline for NLP (CRF) model to automate and enable faster deployments
  • Introduced checkbox and radiobutton data extraction. Used image processing techniques with regex to identify checkboxes and radiobuttons, and extract their corresponding information. Improved the overall extraction accuracy by more than 20%.
  • Built a microservice API to serve clustering model results to the UI. Worked from scratch and pushed it to production in less than 2 weeks.
  • Built REST API to monitor and deploy Google's AI platform models & versions and VertexAI pipelines to higher environments. (DevOps takes responsibilities to deploy models and pipelines in higher environments. Since Developers do not get enough control over such deployments, this API bridges the gap)
  • Interviewed more than 20+ candidates during a recruitment drive.
  • Was part of the hackathon judging panel for SSN Hack & Tackle 2022
  • Took ownership of new product to enable human-in-the-loop (feedback loop) for existing annotation tool. Explored several NLP (like LayoutLM, BERT) and image-based models (YOLO, MaskRCNN) to enable this automation.
  • Coordinated with 2 other MLEs to integrate the pipelines, APIs and models & acted as a SPOC during standups.
📚 What did I learn?
  • Learned to build MLOps pipelines from scratch
  • Explored light-weight ML models like Apache OpenNLP. Compared with existing models cost-wise and resource allocation
  • Learned to build REST APIs with FastAPI
  • Learned to work with Docker images and use them to train models
  • Explored various GCP ML products like AI platform, Vertex AI pipelines
  • Designed database schema for feedback loop and built API endpoints using SpringBoot

➡️ Software Engineer - Trainee @ Mr.Cooper

Full-time . Jul 2021 - May 2022

An upgraded version of my internship. But, I was given more responsibilities and ownership of work items.

⚔️ Key accomplishments
  • An extended version of the annotation-feedback tool, and a tool for correcting misclassified pages of the mortgage documents.
  • Improved the website load up time by almost 50% (from 10 seconds to less than 5 seconds), and reduced the size of the webpack bundle size by 75% (from 2MB to less than 500KB) using Gzip and web compression techniques
  • Added new UX suggestions & implemented on UI to enable search, sort and filter records.
  • Worked on server resource allocation for a PDF bursting microservice that handles millions of PDF files per day. Monitored the service performance and fine-tuned the resources.
📚 What did I learn?
  • Learned to code with lesser bugs during the code reviews.
  • Got an opportunity to present my work to the bigger team.
  • Worked on tasks that were way out of my scope/responsibility - by taking ownership of tasks related to application's performance, resource management, and load testing.
  • Volunteered to be part of a few recruitment drives and as a hackathon judge. (It feels amazing to be on the other side of the interviewee table)
  • Hustled a little more to explore the machine learning side of the product and was seeking opportunities to work on them.
  • Overcame my fear on working with SpringBoot. I voluntarily took up tasks related to it and got better at it.
  • Got a better hands-on my Python and ReactJS skills.
  • Came up with innovative ideas that could possibly improve the UX of the product.

➡️ Graduate Intern @ Mr.Cooper

Internship . Jan 2021 - Jul 2021

Stepped into a new domain altogether. Really grateful to have started my career here as an intern. The learning curve was very steep, and my team never considered me as an "Intern", rather they gave me tasks (opportunities) just like to any other "Software Engineer" in the team.

💼 What is my role?

Worked as a full-stack developer to build UI for document processing tool that uses ML to annotate, cluster, classify, and extract data from mortgage documents. You can check more about it in

📚 What did I learn?
  • In a period of 6 months, I moved from "Nothing" to "Something" in web development using ReactJS.
  • Explored a bit about the mortgage industry as a whole. Learned how mortgage works in the US.
  • Tried learning a little on SpringBoot. But, couldn't get much hold of it.
  • Brushed up my skills on Python - Flask.
🔨 What did I work on?

Built full-stack web apps to annotate, cluster, classify and verify & re-annotate (feedback loop) mortgage documents for the machine learning model to learn, and make more accurate predictions/classifications in the future.

🧰 Technologies used
  • ReactJS
  • SpringBoot
  • Python Flask APIs
  • Google Cloud Platform
  • Azure DevOps

➡️ Content Reviewer @ Section EngEd program

Part-time . Oct 2020 - Present

💼 Role
  • Write technical tutorials as a contributor
  • As a peer-reviewer, review tutorial ideas and approve them
  • Verify basic checklists while submitting a PR
  • Verify for grammar and plagiarism check of tutorials
  • Verify and test code snippets in the tutorials
  • Approve tutorials for publishing
⚔️ Key Accomplishments
  • Written 13 technical tutorials that crossed 100k+ views
  • Reviewed 200+ tutorials and approved them for publishing
  • Reviewed 100+ tutorial ideas before allowing contributors to work on them
  • #2 most active contributor with around 690+ commits editing 35K+ lines in the repo
  • One of the most active contributors in Section's EngEd community
  • Was part of a discussion panel hosted by the Community Manager on "Boosting your Software Career Journey". You can view the post here.

🖥️Section EngEd profile

➡️ Junior Machine Learning Engineer & Community Builder @ Omdena

Part-time . Nov 2021 - Dec 2021

Improving Deepfake Detection Algorithms and Solving the Generalization Gap.

🔨 What did I work on?
  • I read a few research papers to understand existing methods for addressing deepfake problem.

(Discontinued my work on this challenge after a month since I was working something with a higher priority and more opportunistic✨)

🖥️ Problem statement

Part-time . Aug 2019 - Nov 2019

Worked in collaboration with UNHCR and collaborators from 34 different countries, on finding the Internal Displacements at Somalia using Satellite images.

Selected as Community Builder due to exemplary performance on an AI challenge in finding Internal Displacements at Somalia.

(As a community-builder - I have nothing to do in specific😅. It's a role just be a part of an exclusive Slack group for connecting all people working on Omdena challenges)

🔨 What did I work on?
  • Exploratory Data Analysis on displacement datasets from UNHCR

Technically, I didn't learn much, since I was in my sophomore year and wasn't aware of how to work with ML.

🖥️Problem statement

➡️ Project Intern @ Tata Consultancy Services

Internship . Jul 2020 - Sep 2020

🔨 What did I build?

Built a Flutter mobile chatbot application for addressing healthcare related queries from patients, and connecting them with the right doctors for consultation. (A dumb version of Practo😂).

I was new to mobile app development with Flutter. So, I started learning it from scratch by learning:

  • how to design and layout components
  • how to connect with Google's dialogflow for the chatbot
  • worked only on the front-end

PS: Discontinued learning mobile-app development since it was boring tbh 😅.


➡️ Machine Learning Engineer @ Estreetz Technologies Private Limited

Internship . Sep 2018 - Sep 2019

Built a chatbot for business automation (to manage workplace and employees) using an Open Source NLP Platform called RASA. In 2018, the vision of the startup was to focus on automating of task tracking, building a SaaS application for HR management, and other IoT solutions for building an automated workplace.

🔨 What did I learn here?
  • Learned to create a RASA chatbot
  • Integrated it with Android app using RESTful Flask APIs
  • Hosting in AWS EC2
  • Git VCS
  • Deep learning NLP models
  • Business pitching and presentation

🖥️ Estreetz Website & Estreetz on AngelList

➡️ Student Machine Learning Researcher @ Smart Space Labs - Amrita University

Part-time . Sep 2017 - Sep 2019

With focus on digital buildings, digital campus and digital city, this lab has been setup in 2017 by the Department of Computer Science and Engineering with funding from Department of Science and Technology, Government of India under project id: F.NO NRDMS/01/175/016 G & C.

🛠️ What areas did I work on?

I worked on object tracking, fire detection, smoke, and water leakage detection through image processing.

🔨 What did I work on?

1) Data collection for object tracking, smoke, and water leakage detection 2) Data annotation for object tracking and smoke detection 3) Analyzed performance metrics for various deep learning models 4) Multi-linear regression models for prediction of spread of fire 5) Implemented Convolutional Neural Networks for detection of cars

🚧 Final Project:

Implemented an fire detection algorithm through real-time video processing to detect fire-spread.

🖥️ GitHub repo

➡️ Internshala Student Partner 12.0 @ Internshala

Internship . Dec 2018 - Feb 2019

Digital and social media marketing of products like Internshala trainings and internships.

🔨 What did I learn here?

I learned about digital marketing.

⛏️ What did I actually learn?

Nothing. Just posting their products in all my social media accounts. Not sure why I have this under my experiences 😂

🖥️ Certificate