About
Accomplished AI/ML Engineer with 4 years of expertise, specializing in designing, developing, and deploying enterprise-scale machine learning and deep learning solutions across finance, legal, and technology domains. Proficient in building and fine-tuning NLP and transformer-based models, including BERT, GPT, and custom LLMs, to deliver real-time inference and actionable business insights. Leverages MLOps, distributed processing, and advanced NLP techniques to optimize model accuracy, reduce operational costs, and enhance decision-making.
Work
JPMorgan Chase&co
|AI/ML Engineer
Alabama, Alabama, US
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Summary
Leads the development and deployment of enterprise-scale AI/ML and deep learning solutions, optimizing data pipelines and enhancing model performance for critical financial and legal applications.
Highlights
Curated and preprocessed over 5M internal documents using Python (pandas, NumPy, spaCy, NLTK), improving input quality and reducing irrelevant model outputs by 25%.
Built and fine-tuned PyTorch-based LLMs using Hugging Face Transformers, reducing manual content preparation time by 35% for 60,000+ employees via real-time content generation.
Integrated FAISS-based retrieval-augmented generation (RAG) with LLM pipelines, increasing factual accuracy of outputs by 20% through relevant internal document referencing.
Optimized GPU cluster training pipelines on AWS with incremental retraining strategies, reducing model training time by 40% and enabling weekly model updates.
Led A/B testing of model prompts and retrieval strategies, achieving a 25% reduction in manual editing and improved trust in AI-generated content.
IBM
|Machine Learning Engineer
Unknown, Unknown, India
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Summary
Designed and implemented end-to-end NLP pipelines and machine learning models, significantly reducing manual effort and enhancing accuracy for contract analysis and risk assessment.
Highlights
Designed end-to-end NLP pipelines using spaCy and NLTK, structuring over 50,000 unstructured contracts and reducing manual review effort by 60%.
Developed and trained scikit-learn models for clause classification and risk assessment, achieving 92% accuracy on unseen contract data.
Optimized ETL workflows and data storage in SQL Server, reducing query execution time by 40% and enabling efficient retrieval of structured contract data.
Implemented automated model retraining pipelines using Python, AWS S3, and Docker, maintaining over 90% model accuracy over 2.5 years in production.
Optimized processing pipelines for scalability, reducing total contract batch processing time from 4 hours to 1.5 hours, enabling near real-time analysis.
Education
University of Alabama at Birmingham
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Master of Science
Computer Science
Nagpur University
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Bachelor of Engineering
Computer Science and Engineering
Skills
Methodologies
SDLC, Agile, Waterfall.
Programming Language
Python, Java, SQL, Bash, JavaScript, HTML, CSS.
ML/DL Frameworks
TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM, CatBoost, Hugging Face Transformers.
AI & ML Techniques
Regression, Random Forest, Association Rules, Support Vector Machine, Logistic Regression, K-Means, Clustering, Classification, SVM, KNN.
Deep Learning and NLP
Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTMs, Text Preprocessing, Named Entity Recognition (NER), Sentiment Analysis, Transformer Models (BERT, GPT).
Web & API Development
Django, Flask, REST APIs, Bootstrap.
Data Processing
Pandas, NumPy, Dask, Apache Spark, PySpark, Data Structures, Algorithms.
Data Engineering
Apache Airflow, Kafka, ETL Pipelines, Data Lakes, Feature Store, MLflow.
Database Management
PostgreSQL, MySQL, MongoDB, BigQuery, Redis, Database Management.
Cloud & DevOps
AWS (SageMaker, EC2, Lambda, S3), Docker, Kubernetes, Linux, Git, CI/CD.
Other tools/ Skills
Tableau, Power BI, Prometheus, Grafana, ELK Stack, TensorBoard, PyTest, UnitTest, Postman, A/B Testing, Data Validation, Data Visualization, OOPS, Design Thinking.