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ashishallu/README.md

About Me

I'm an AI/ML Engineer, Data Scientist, and Software Engineer finishing my B.E. in Artificial Intelligence and Data Science at CBIT Hyderabad. I design and ship production-style systems — models that explain their own decisions, pipelines that hold up at scale, and APIs built the way a real engineering team would build them — using open-source stacks with zero paid dependencies.

  • 🔬 Explainable AI systems (SHAP, LIME, Grad-CAM) for high-stakes, regulated domains
  • 🌊 Graph-based fraud detection at streaming scale (Kafka + PySpark + GNNs)
  • 🧠 Full-stack ML — from model training to FastAPI microservices and deployable APIs
  • 🏆 Best Use Case Award — SRM-AP Quantum Hackathon (Post-Quantum Cryptography)

Tech Stack

Languages & Core

Machine Learning & Explainability

Data, Retrieval & Streaming

Apps & Infrastructure

Highlights

  • 🕸️ Engineered a real-time fraud detection pipeline over streaming transaction graphs — Kafka + PySpark + heterogeneous GNNs, fully explained with SHAP
  • 📑 Built a hybrid-retrieval RAG assistant (BM25 + dense embeddings + cross-encoder reranking) for financial compliance Q&A
  • 🩺 Shipped a multimodal diagnostic assistant combining ResNet-50 + Grad-CAM visual reasoning with a BioBERT-powered RAG layer
  • 🧪 Found and fixed a subtle data-leakage bug in a clinical trial model, then tuned it for real-world reliability
  • 🏆 Directed a national-scale hackathon as Events Head, CBIT Student Technical Association
  • 🌍 Every project shipped fully open-source, zero paid dependencies — data pipeline to deployed demo

Certifications

  • AWS Certified CloudOps Engineer – Associate
  • IBM Enterprise Data Science
  • Oracle AI Foundations Associate
  • Business Analytics — Skill India (NSDC)

What Drives Me

I'm the kind of engineer who reads the SHAP values before celebrating the accuracy score — I want to know why a model decided what it decided, not just that it got there. That curiosity is what pulls me toward explainable and responsible AI, and it's why I like sitting close to the whole problem: framing it, training the model, then building the engineering around it that makes it trustworthy enough to actually use. I'm energized by systems where the stakes are real — fraud, healthcare, compliance — the kind of domains where getting it right actually matters. Long-term, that same curiosity is pointed at a PhD, and eventually, building something of my own in deep-tech.


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  1. fraud-detection-pipeline fraud-detection-pipeline Public

    Real-time financial fraud detection pipeline using Kafka, Spark Streaming, machine learning, and Docker to identify suspicious transactions with low-latency processing and live monitoring.

    Python 11

  2. medivision.AI medivision.AI Public

    Explainable chest X-ray diagnostic assistant - ResNet-50 classifier + Grad-CAM + RAG (ChromaDB, BioBERT) over clinical guidelines, drafted into reports by a local Ollama/Mistral-7B LLM. Fully open-…

    Python 6

  3. healthcare-microservices-platform healthcare-microservices-platform Public

    Two independent FastAPI microservices (Patient Records and Appointments) with separate PostgreSQL databases, communicating via REST for sync checks and Redis pub/sub for async notifications. Inclu…

    Python 8

  4. jpmc-intelligence jpmc-intelligence Public

    RAG-Powered Financial Query Assistant for JPMorgan Chase documents with Hybrid Search, Docker support, and Streamlit UI

    Python 10