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Ashrith Vavillapally

@ashrithvavillapally

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Software Engineer and full-stack developer building cloud-ready products and ML-powered solutions.

India

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What I'm looking for

I’m looking to build scalable full-stack and backend systems on AWS, with room to apply ML/LLMs (including RAG). I want a team that values clean APIs, reliable deployment, and continuous improvement through feedback.

I’m a Software Engineer with experience building cloud-deployed full-stack applications, backend services, and machine learning solutions. I focus on scalable, production-ready systems, backed by strong fundamentals in DSA, OOP, DBMS, Operating Systems, and Computer Networks.

In internships, I shipped practical AI and ML outcomes: I optimized 250+ AI prompts to automate educational content generation and reduced production time by 40%. I also built ML pipelines on 500,000+ records for demand forecasting, improving forecasting performance by 14% and achieving strong evaluation results.

I enjoy end-to-end development—designing REST APIs, authentication, and role-based access control, containerizing services, and deploying on AWS. I also develop LLM/RAG systems for semantic search and context-aware Q&A, engineering fast retrieval using FAISS and ChromaDB.

Experience

Work history, roles, and key accomplishments

AI Prompt Engineer Intern

Easy2Learn Technologies

Jan 2026 - Apr 2026 (3 months)

Developed and optimized 250+ system and few-shot prompts for GPT-4o, Claude 3.5 Sonnet, Midjourney v6, and Stable Diffusion XL to automate educational content generation. Produced 1,500+ learning assets and reduced content production time by 40% using AI-assisted automation pipelines, improving output quality via prompt iteration and user feedback analysis.

Prompt Engineering GPT 4o Few Shot Learning Midjourney Automated Content Generation Quality Evaluation

Machine Learning Engineer Intern

Prodigy Infotech

May 2025 - Jul 2025 (2 months)

Built and optimized ML pipelines using Python, Scikit-Learn, XGBoost, and AWS to process 500,000+ sales records for demand forecasting and predictive analytics. Improved forecasting performance by 14% over baseline models through feature engineering and hyperparameter tuning, achieving ~1,650 WMAE and an R score of 0.91.

Python scikit learn XGBoost AWS Feature Engineering Hyperparameter Tuning Demand Forecasting Predictive Analytics Model Evaluation

Software Developer Intern

Trolla

Feb 2025 - May 2025 (3 months)

Developed a full-stack customer service platform using React.js, Node.js, MongoDB, and AWS, supporting complaint management workflows for 5,000+ concurrent users. Built and documented 20+ authenticated REST API endpoints with RBAC and workflow automation, and containerized/deployed services on AWS EC2 while optimizing database operations and API performance.