Hello!

I am Ankitha Suresh

Engineer, Innovator & Wanderlust

About Me

Who am i?

Hey there! 👋 I’m a software engineer who enjoys building systems that are scalable, reliable, and efficient. My focus is on solving complex problems through automation, resilient design, and creative use of technology.

Curiosity fuels my work in software and DevOps, where challenges become opportunities to innovate and grow.

Outside of work, I carry that same explorer’s mindset; always eager to discover, connect, and improve.

My Skills

What am I good at?

Python
90%
Java
80%
AWS
85%
Docker & Kubernetes
90%
CI/CD
85%
REACT
90%

Experience

Headstarter 07/2024 - 08/2024

Software Engineer

  • Built and released an open-source REST API for Microsoft Dynamics 365 CRM, streamlining data sync across 6 modules and cutting manual updates by 85%.
  • Deployed Node.js microservices (GraphQL, WebSocket) with Docker and GitLab CI/CD, enabling seamless OpenAI-powered feature rollouts with zero downtime.
  • Designed a predictive analytics system on AWS CDK and PostgreSQL, handling 500 Mbps traffic and 4,000+ daily transactions for accurate inventory forecasting.

Hewlett Packard Enterprise 09/2021 - 07/2023

Software Engineer

  • Proposed and delivered multi-cloud Infrastructure-as-Code using Terraform and Ansible, deploying Kubernetes clusters across AWS, Azure, and on-prem, reducing downtime by 40%.
  • Automated disaster recovery drills with Python and Ansible on Linux, verifying standby readiness 5x faster across 50+ clusters.
  • Orchestrated zero-downtime rolling upgrades with Jenkins CI/CD, automating sequential node patching, and reducing maintenance windows by 50%.
  • Engineered high-availability REST APIs in Spring Boot, improving latency by 45% and enabling automated failover with 99.99% uptime.

Hewlett Packard Enterprise 02/2021 - 08/2021

Research and Development Engineer

  • Delivered an automated Python installation tool that reduced system setup time by 90% and was adopted widely across engineering teams.
  • Integrated Prometheus & Grafana monitoring into Kubernetes High Availability clusters, building dashboards and alerts that fostered shared observability and reduced MTTR by 60% through faster issue detection.
  • Automated disaster recovery drills using Python and Ansible, verifying standby readiness 5x faster across 50+ clusters.
My Work

what have I done?

Crop Yield Prediction GitHub

Used Python, scikit-learn, and pandas to analyze a 5-year agricultural dataset. By adapting the Gradient Boosting Algorithm and employing feature scaling and selection techniques, I achieved a 91.8% accuracy in predicting crop yields. Additionally, I developed a Heroku-powered mobile app that determines suitable crops and predicts yield percentages based on user-provided location data, optimizing agricultural productivity by up to 75%.

Buddy: AI Chatbot Github

It is an intelligent conversational AI built with Next.js and React, using OpenAI’s models for accurate responses. It features Retrieval-Augmented Generation (RAG) to pull from a knowledge base, user login for personalized interactions, and a feedback mechanism for rating responses. Leveraging Pinecone for knowledge management and MongoDB for data storage, the application is efficiently deployed on Vercel.

Car Make and Model Classification Github

Engineered a highly efficient machine learning model using Convolutional Neural Networks (CNNs) to analyze a vast dataset of car images, achieving precise prediction of car make and model. By implementing the Inception-v3 model, I attained a 92% accuracy rate, which facilitated the development of an advanced image recognition system for automotive applications.

ZenMood GitHub

MoodFeed is a feature that allows users to select their current mood, and based on that mood, view videos that align with their emotional state. This feature integrates seamlessly with an Instagram-like app, offering users a dynamic and personalized video experience.

Algocards GitHub

Algocards is an AI-powered tool that generates flashcards for mastering Data Structures and Algorithms (DSA). It offers three sets: Basic DSA, Algorithms, and Advanced DSA, along with assessments to test user knowledge. Built with Next.js and React, Algocards provides an interactive learning experience for DSA enthusiasts.

Crop Yield Prediction GitHub

Used Python, scikit-learn, and pandas to analyze a 5-year agricultural dataset. By adapting the Gradient Boosting Algorithm and employing feature scaling and selection techniques, I achieved a 91.8% accuracy in predicting crop yields. Additionally, I developed a Heroku-powered mobile app that determines suitable crops and predicts yield percentages based on user-provided location data, optimizing agricultural productivity by up to 75%.

Buddy: AI Chatbot Github

It is an intelligent conversational AI built with Next.js and React, using OpenAI’s models for accurate responses. It features Retrieval-Augmented Generation (RAG) to pull from a knowledge base, user login for personalized interactions, and a feedback mechanism for rating responses. Leveraging Pinecone for knowledge management and MongoDB for data storage, the application is efficiently deployed on Vercel.

Car Make and Model Classification Github

Engineered a highly efficient machine learning model using Convolutional Neural Networks (CNNs) to analyze a vast dataset of car images, achieving precise prediction of car make and model. By implementing the Inception-v3 model, I attained a 92% accuracy rate, which facilitated the development of an advanced image recognition system for automotive applications.

ZenMood GitHub

MoodFeed is a feature that allows users to select their current mood, and based on that mood, view videos that align with their emotional state. This feature integrates seamlessly with an Instagram-like app, offering users a dynamic and personalized video experience.

Algocards GitHub

Algocards is an AI-powered tool that generates flashcards for mastering Data Structures and Algorithms (DSA). It offers three sets: Basic DSA, Algorithms, and Advanced DSA, along with assessments to test user knowledge. Built with Next.js and React, Algocards provides an interactive learning experience for DSA enthusiasts.

Crop Yield Prediction GitHub

Used Python, scikit-learn, and pandas to analyze a 5-year agricultural dataset. By adapting the Gradient Boosting Algorithm and employing feature scaling and selection techniques, I achieved a 91.8% accuracy in predicting crop yields. Additionally, I developed a Heroku-powered mobile app that determines suitable crops and predicts yield percentages based on user-provided location data, optimizing agricultural productivity by up to 75%.

Contact

Get In Touch!

+917619680294
s.ankitha0904@gmail.com