Rachana Jena

AI Engineer

Hyderabad, India 0+ yrs exp 81 · Excellent

About

Motivated AI Engineer and Machine Learning Engineer fresher with hands-on internship experience building end-to-end ML and Generative AI solutions. Proficient in Python, Deep Learning, NLP, and LLMs, with practical exposure across the full ML lifecycle — data preprocessing, model development, hyperparameter tuning, and production deployment. Delivered a 12% improvement in forecasting model performance and reduced API latency by 30% through pipeline optimization. Experienced in RAG pipeline design, LangChain, LangGraph, Hugging Face Transformers, and vector database integration. Seeking a full-time role as an AI Engineer, AI/ML Engineer, or Data Scientist to apply and grow these capabilities in a production environment.

Skills & Expertise (41)

Python Advanced

9.1/10

Years Exp

Hyperparameter Tuning Advanced

8.5/10

Years Exp

Model Training Advanced

8.5/10

Years Exp

PyTorch Advanced

8.0/10

Years Exp

TensorFlow Advanced

8.0/10

Years Exp

scikit-learn Advanced

8.0/10

Years Exp

Jupyter Notebook Advanced

8.0/10

Years Exp

SQL Intermediate

7.5/10

Years Exp

Flask Intermediate

7.5/10

Years Exp

Git Intermediate

7.5/10

Years Exp

REST APIs Streamlit GitHub VS Code Reproducibility Model Versioning MLflow Tableau Power BI Matplotlib Statistical Analysis Feature Engineering Data Cleaning EDA LangGraph CNN LSTM YOLO OpenCV Supervised Learning Model Evaluation Hugging Face Transformers LangChain NumPy RAG Pipelines LLM Integration Prompt Engineering Vector Databases Pinecone Semantic Search Pandas

Work Experience

Machine Learning Engineering Intern

Insight Fusion Analytics

May 2025 - Oct 2025

Engineered LSTM-based time-series forecasting models for stock price prediction, reducing Root Mean Square Error (RMSE) by 12% through systematic hyperparameter tuning and advanced feature engineering for temporal pattern recognition. Built end-to-end ML pipelines for large-scale financial time-series data processing using Pandas and NumPy, and developed Flask-based REST APIs to serve real-time model predictions. Implemented MLflow experiment tracking to monitor RMSE, MAE, and model versions — enabling full reproducibility, structured model comparison, and accelerated iteration cycles. Reduced API response latency by 30% through model optimization and efficient REST API architecture design, improving real-time inference performance. Collaborated with cross-functional teams to align machine learning model outputs with business objectives and stakeholder requirements.