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
95%
Java
90%
Cloud
90%
Docker & Kubernetes
90%
System Design & Microservices
85%
Databases
80%
DevOps & CI/CD
85%

Experience

Headstarter 07/2024 - Present

Software Engineer

  • Integrated generative AI systems using OpenAI APIs and RAG pipelines to power 1000+ AI-driven interview sessions daily with robust secure API orchestration, rate limiting, and fault tolerant service design.
  • Architected a secure, containerized code-execution platform using Docker and Kubernetes, reducing container startup latency by 40% while supporting 10,000+ coding submissions monthly.
  • Developed scalable distributed backend services using Node.js, REST and GraphQL APIs supporting 5,000+ active users with sub-200ms latency in high-availability production environments.
  • Engineered a real-time WebSocket service using Node.js and Redis supporting 500+ concurrent sessions with sub-50ms latency, enabling reliable real-time communication in production-grade systems.

Hewlett Packard Enterprise 09/2021 - 07/2023

Software Engineer

  • Engineered quorum arbitration and I/O fencing algorithms using Python and C for distributed cluster coordination, reducing latency by 98% and ensuring data consistency across 100+ production clusters.
  • Proposed and delivered multi-cloud Infrastructure platform using Terraform, Ansible and Kubernetes across AWS, Azure, and on-prem environments, reducing downtime by 40% while enabling scalable service deployments.
  • Implemented replication-aware failover mechanisms across MySQL and NoSQL distributed databases achieving 20s RTO with zero data loss in mission-critical systems.
  • Designed and deployed Java-based microservices and high-availability REST APIs using Spring Boot, improving system latency by 45% while maintaining 99.99% uptime across enterprise deployments.
  • Co-led cross-functional teams across engineering, product, and operations to deliver distributed platform features, accelerating release cycles by 86% and improving system reliability.

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?

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.

Contact

Get In Touch!

+917619680294
s.ankitha0904@gmail.com