Open to opportunities

Samiksha Abdar

@samikshaabdar

Message

AI/ML engineer intern turning data into accurate, deployable models and RAG applications.

India

Message

What I'm looking for

I’m looking for a role where I can build and deploy ML/AI systems—especially RAG and predictive models—using strong data pipelines and clean APIs, with mentorship and room to grow from internship into measurable product impact.

I’m an AI/ML engineer intern focused on building practical machine learning solutions—from data pipelines to deployed models. At IIT Bombay, I engineered and deployed a hybrid recommendation engine using feature engineering and threshold optimization, improving recommendation quality to 0.78 Precision@K, and I built a churn prediction framework with gradient boosting that reached 85% prediction accuracy.

In my AI/ML internship at MedTourEasy, I created end-to-end preprocessing pipelines on 10K+ healthcare records using Python, Pandas, and NumPy, improving data quality and reducing data ingestion time by 40%. I also developed Power BI dashboards that accelerated analytical reporting by 30%—and through my projects, I’ve leaned into Generative AI and RAG (FAISS + LangChain/FastAPI) to reduce query latency by 40% and achieve 90% query relevance accuracy.

Experience

Work history, roles, and key accomplishments

AI/ML Intern

MedTourEasy

Oct 2025 - Mar 2026 (5 months)

Built end-to-end data preprocessing and transformation pipelines on 10K+ healthcare records using Python, Pandas, and NumPy, improving data quality and reducing data ingestion time by 40%. Conducted EDA and delivered interactive Power BI dashboards to analyze healthcare trends and accelerate KPI reporting by 30%.

data preprocessing data transformation Python Pandas NumPy Power BI Healthcare Analytics KPI Reporting Data Quality

Machine Learning Intern

IIT Bombay

Jan 2025 - Jun 2025 (5 months)

Engineered and deployed a hybrid recommendation engine on 10K+ user-item interactions, improving Precision@K to 0.78 through feature engineering and threshold optimization. Built a gradient-boosting churn prediction framework (85% accuracy) and processed 15K+ learner records using Python, Pandas, and SQL to deliver clean, model-ready features.

Feature Engineering Precision@K Optimization Churn Modeling Gradient Boosting Python Pandas SQL predictive modeling

Education

Degrees, certifications, and relevant coursework

D.Y. Patil College of Engineering and Technology

Bachelor of Technology, Computer Science and Engineering

Grade: CGPA: 8.8 / 10

Bachelor of Technology in Computer Science and Engineering with a CGPA of 8.8/10. Completed coursework in machine learning, artificial intelligence, deep learning, NLP, data structures and algorithms, data science, probability and statistics, database management systems, and cloud computing.