Akshith Sai
Kondamadugu
Building intelligent systems at the intersection of LLMs, Agentic AI, and cloud infrastructure. CS Engineering graduate (2026) with two AWS certifications and real production experience.
Who I am
Final-year B.E. student in Computer Science at MVSR Engineering College, Hyderabad, specializing in IoT, Cyber Security & Blockchain. Graduating 2026 with a focus on production-ready AI systems.
Large Language Models, Retrieval-Augmented Generation, multi-agent orchestration with LangGraph & CrewAI, and deploying ML pipelines on AWS SageMaker and Google Cloud Platform.
I build AI systems end-to-end — from data ingestion and model training to REST API deployment and monitoring. Production quality, not just prototype demos.
Certified AWS Machine Learning Engineer and AI Practitioner. Completed multi-AI agent systems coursework from DeepLearning.AI and a Data Science program via HarvardX.
Where I've worked
- Designed, trained, and evaluated supervised learning models in TensorFlow on GCP across regression, classification, and deep learning use cases with measurable accuracy gains through systematic hyperparameter tuning.
- Built a computer vision object-recognition pipeline using CNNs, image preprocessing, data augmentation, and transfer learning to improve generalization on held-out test sets.
- Executed end-to-end ML workflows: dataset preparation, feature engineering, cross-validation, inference, and result interpretation in reproducible engineering format.
- Documented experiment findings, model metrics, and architecture decisions aligned with production data science standards.
- Built and deployed machine learning models using Amazon SageMaker with built-in algorithms and custom training scripts; served models as REST API endpoints.
- Integrated Amazon S3 for versioned data storage; used CloudWatch and SageMaker Clarify for performance monitoring, data drift detection, bias analysis, and model explainability.
- Executed the complete ML lifecycle on AWS: data ingestion, EDA, feature engineering, model training, evaluation, API deployment, and post-deployment monitoring.
- Applied MLOps best practices including model versioning, fairness checks, bias detection, and explainability auditing in a cloud-native environment.
Things I've built
Tech I work with
Credentials
Let's connect
Open to full-time roles, internships, and collaborations in AI/ML engineering.
akshithsai24@gmail.com