Jagadeesh Arigala
Professional Summary
AI/ML Engineer with hands-on internship and project experience building end-to-end deep learning pipelines, Generative AI systems, and Computer Vision applications. Demonstrated ability to architect Hybrid Graph-RAG platforms, deploy multi-agent AI systems on Azure, and train CNN-based classifiers achieving up to 94% accuracy on medical imaging datasets. Proficient in TensorFlow, PyTorch-compatible workflows, LLM integration, and MLOps practices including experiment tracking, model versioning, and Docker-based containerization. Built full-stack AI solutions integrating ONNX embeddings, knowledge graphs, and vector search to serve real industrial retrieval use cases. Pursuing B.Tech in CSE with AI and ML specialization at VIT-AP University, graduating May 2026, with a GPA of 8.51 and certifications across Azure AI, Generative AI, and Python programming.
Technical Skills
Work Experience
- Designed and trained a multi-class CNN classifier for skin disease diagnosis across 9 disease categories using a 27,000-image dermoscopy dataset, achieving 94% test accuracy.
- Applied transfer learning with custom architecture fine-tuning to maximize classification performance on imbalanced dermatological image classes.
- Structured end-to-end model development workflows using MLOps principles including experiment tracking and model versioning to ensure full reproducibility across training iterations.
- Containerized the model training and inference environment using Docker, enabling consistent execution across development and evaluation stages.
- Evaluated model generalization across held-out test splits, analyzing per-class precision and recall to identify failure modes in visually similar disease categories.
- Documented training configurations, hyperparameter choices, and evaluation results to maintain a reproducible and auditable development record aligned with MLOps best practices.
Projects
- Architected a Hybrid Graph-RAG system fusing knowledge graph traversal with vector search, automatically extracting 7 entity types and 464 relationships from heterogeneous industrial documents including PDF, DOCX, and XLSX formats using spaCy NER.
- Delivered 3–5× higher retrieval precision over standard RAG pipelines by combining structured graph relationships with dense vector similarity during query resolution.
- Engineered and deployed a full-stack multi-agent AI platform on Azure App Service with Docker containerization and GitHub Actions CI/CD, enabling automated deployment across environment changes.
- Integrated local ONNX embeddings using the all-MiniLM-L6-v2 model to eliminate external API dependency, reducing per-query embedding cost to zero.
- Reduced industrial information retrieval time from 23 minutes of manual search to under 3 seconds — a 99.6% reduction — across maintenance root cause analysis, compliance gap detection, and equipment history workflows.
- Engineered a deep learning pipeline using MobileNetV3Small with CBAM attention in TensorFlow, achieving 89% test accuracy on the APTOS 2019 retinal fundus dataset for 5-grade DR severity classification.
- Implemented OpenCV CLAHE preprocessing to enhance retinal image contrast and reduce noise before model ingestion, improving feature separability across severity grades.
- Applied Albumentations-based augmentation strategies combined with class-weighted loss functions to address severe grade imbalance in the training distribution.
- Benchmarked cross-dataset generalization on 50,000+ EyePACS images without retraining, achieving 83% accuracy and validating the model's real-world transferability across different imaging equipment and populations.
Education
Certifications
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