🚀 Junior AI / Machine Learning Engineer focused on building end-to-end applied ML systems — from data and modeling to deployment-ready AI applications.

I specialize in turning machine learning models into usable products through APIs, scalable pipelines, and real-world decision systems.

📍 India
🎓 B.Tech CSE (AI/ML) — SRM University AP
💼 Open to: Junior ML Engineer | AI Engineer | Data Scientist roles (2026)

I work at the intersection of Machine Learning Engineering + Applied AI:

• ✅ End-to-end ML systems (data → model → API → deployment)
• ✅ Retrieval-Augmented Generation (RAG) applications
• ✅ Decision-focused predictive modeling
• ✅ Explainable AI & model evaluation
• ✅ Production-ready inference services

My goal is simple:
Build ML systems that actually get used — not just trained.

End-to-End Production ML System

• Built telecom churn prediction platform using XGBoost, capturing 72.5% churners while targeting only 35.5% customers
• Designed explainable decision system using SHAP + customer segmentation & profiling to generate persona-driven retention actions
• Deployed production ML service using FastAPI, Docker, and AWS EC2 with MLflow tracking, PostgreSQL logging, and Streamlit dashboard

Tech: Scikit-learn · FastAPI · Docker · AWS · MLflow · PostgreSQL

👉 Demonstrates:

• Applied ML engineering
• Decision-focused data science
• Production deployment

LLM Application with Semantic Search

• Built document-grounded QA system using LangChain + FAISS
• Implemented semantic retrieval with MMR to reduce hallucinations
• Returned answers with source citations
• Dockerized FastAPI service deployed on AWS
• Debugged real-world memory constraints during local LLM inference

Tech: LLMs · LangChain · Hugging Face · FAISS · FastAPI · Docker · AWS

👉 Demonstrates:

• Applied NLP & LLM engineering
• RAG architecture
• Production API design

Python • SQL

scikit-learn • XGBoost • TensorFlow • PyTorch
Feature Engineering • Model Evaluation • EDA • ML Pipelines

LLMs • RAG • LangChain • Transformers
NLTK • spaCy • Semantic Search

Pandas • NumPy • Data Analysis • Visualization
Matplotlib • Seaborn

FastAPI • Docker • REST APIs
AWS (EC2, S3) • MLflow • Git

OpenCV • CNNs • Image Processing

• Write reproducible ML pipelines
• Treat models as software systems
• Measure business impact, not just accuracy
• Prefer simple models before complex ones
• Build deployable solutions early

• 🥇 Best Paper Award — ICAIN 2025
  ESADN: Enhanced Spatial Attention Network for Road Accident Detection

• 🥇 Gold Medalist — ProductKraft Expo 1.0

• 🤖 ML Research Intern — SRM University AP

• 🤖 AI Intern — Zebo.ai

• Production ML system design
• LLM application engineering
• Model monitoring & evaluation
• Scalable inference architectures
• Advanced feature engineering

• LinkedIn: https://linkedin.com/in/arjun-pramod
• Email: arjunpramod509@gmail.com

⭐ Pinned repositories below represent my strongest end-to-end AI/ML projects.