AI/ML Engineer - Credit Risk & AI Systems Specialist

SHUBH RANA AI/ML Engineer

Texas, USA 361-***-**** ************@*****.*** LinkedIn

Professional Summary

AI/ML Engineer with 4+ years of experience designing, developing, and deploying machine learning and AI-based solutions across banking, geospatial systems, and full-stack applications. Strong expertise in building end-to-end AI/ML pipelines, predictive modeling, data engineering, and deploying production-grade ML systems.

Experienced in credit risk modeling, customer analytics, classification and regression models, and AI-driven decision systems, with hands-on exposure to cloud data platforms, model monitoring, and deployment frameworks. Adept at translating complex business problems into scalable AI/ML solutions with measurable impact.

Core Skills

AI/ML & Data Science: Machine Learning, Deep Learning, Predictive Modeling, Classification, Regression, Clustering, Feature Engineering, NLP, Generative AI, LLM Applications, Prompt Engineering, RAG, Logistic Regression, Random Forest, XGBoost, Decision Trees Frameworks & Libraries: Scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, LangChain, Pandas, NumPy, Matplotlib LLM & Vector Technologies: OpenAI APIs, FAISS, Pinecone, ChromaDB, Sentence Transformers, Embeddings, Semantic Search, Vector Databases Programming & Databases: Java, Python, SQL (MySQL), Pandas, NumPy, MongoDB Data Engineering & Big Data: Apache Spark, Spark SQL, Kafka, Hadoop, ETL/ELT Pipelines, Data Warehousing Cloud & Platforms: Snowflake, AWS, Azure, Databricks MLOps & Deployment: FastAPI, Flask, Docker, MLflow, Airflow, CI/CD Pipelines, REST APIs, Model Deployment, Model Monitoring Data Visualization & Tools: Tableau, Power BI, Git, GitHub, Jupyter Notebook, VS Code, PyCharm Professional Experience

AI/ML Engineer – Retail Banking & Lending January 2026 – Present RBFCU Texas, USA

• Designed and deployed AI/ML solutions for credit risk assessment, loan default prediction, and underwriting automation using XGBoost, Scikit-learn, FastAPI, Snowflake, and Docker.

• Built Retrieval-Augmented Generation (RAG) pipelines using LangChain, LLMs, and vector databases to automate borrower profiling, financial document analysis, loan summarization, and conversational risk insights for lending teams.

• Implemented production-grade ML pipelines including feature engineering, SMOTE balancing, AUC-ROC evaluation, SHAP explainability, CI/CD deployment, model monitoring, and real-time inference APIs to support scalable AI-driven financial decision systems. Machine Learning Engineer July 2022 – December 2023 Persistent Systems, INDIA

• Worked on machine learning and geospatial analytics solutions using Python, SQL, and Apache Spark for processing large -scale structured and location-based datasets.

• Developed predictive analytics and intelligent data processing workflows for government and enterprise applications, including anomaly detection, classification models, and automated reporting systems.

• Integrated early-stage NLP and AI-powered automation features such as document summarization, chatbot assistance, and intelligent search capabilities using OpenAI APIs and vector-based retrieval techniques. Machine Learning Engineer (junior) February 2021 – June 2022 Tech Mahindra India

• Developed and deployed machine learning models for customer churn prediction, sales forecasting, and business analytics using Python, Scikit- learn, SQL, and Pandas on structured enterprise datasets.

• Built ETL pipelines and automated data preprocessing workflows for large-scale transactional data, improving reporting efficiency and supporting analytics-driven decision making.

• Collaborated with software and business teams to integrate ML models into internal web applications through REST APIs, improv ing operational automation and predictive reporting capabilities.

Education

Masters in Computer & Information Science 2024 - 2025 Texas A&M University Corpus Christi, TX USA

Projects

1. AI-Powered Credit Risk & Loan Default Prediction Platform Technologies: Python, Scikit-learn, XGBoost, SQL, Snowflake, FastAPI, Docker, SHAP

• Developed and deployed an end-to-end machine learning platform for loan default prediction using XGBoost and Logistic Regression, improving credit risk assessment and underwriting decision-making.

• Performed feature engineering, SMOTE balancing, hyperparameter tuning, and AUC-ROC evaluation to enhance model accuracy, reduce false positives, and improve risk segmentation performance.

• Built scalable FastAPI-based REST services with Docker deployment and implemented SHAP-based explainability dashboards for transparent real- time loan risk scoring.

2. LLM-Powered Resume Screening & Candidate Matching System Technologies: Python, LangChain, Hugging Face, Sentence Transformers, FAISS, FastAPI, Streamlit

• Built an AI-driven resume screening platform using NLP, embeddings, and LLM-based semantic search to automate candidate ranking and job-role matching workflows.

• Developed a RAG pipeline using LangChain and FAISS enabling recruiter-focused resume querying, AI-generated candidate summaries, and contextual skill-gap analysis.

• Designed an interactive Streamlit dashboard with FastAPI integration for resume uploads, intelligent filtering, and automated hiring recommendations to improve recruiter efficiency.

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