AI/ML Engineer with LLM & MLOps Expertise

MONISHA NETTEM

404-***-**** • *******.*@***********.***• LinkedIn • Georgia, USA (Open to Relocation) PROFESSIONAL SUMMARY

AI/ML Engineer with 4+ years of experience building and deploying machine learning, generative AI, and large language models (LLMs). Skilled in Python, TensorFlow, PyTorch, and scikit-learn for predictive analytics, NLP, computer vision, and generative AI applications. Experienced in data preprocessing, feature engineering, model optimization, evaluation, and deploying models using MLOps pipelines on Amazon Web Services (AWS) and Google Cloud Platform (GCP). EDUCATION

Master of Science in Computer Science Kennesaw State University, Atlanta, Georgia (GPA: 3.8/4.0) May 2025 Bachelor of Technology in Computer Science RVR & JC College of Engineering, Guntur, India (GPA: 3.6/4.0) June 2023 SKILLS

Programming Language/IDEs: Python, R Programming, SQL, Jupyter Notebook, Google Colab Machine Learning: Regression Models, Decision Trees, Random Forests, Naive Bayes, Cohort Analysis, Hypothesis Testing GenAI: LLaMA, Mistral, LlamaIndex, GANs, LangChain AI & Deep Learning: CNN, ANN, BERT, GPT-4, Large Language Model (LLM), Retrieval-Augmented Generation (RAG) Natural Language Processing: Named Entity Recognition (NER), ROBERTa, Claude, Sentiment Analysis Cloud: AWS (S3, Lambda, Glue, Athena, AWS Kinesis, Redshift), GCP (Vertex AI, Google Cloud Storage) Visualizations: Tableau, Power BI (DAX, Power Query), Looker, Excel Packages and Frameworks: NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras, OpenCV, NLTK, XGBoost, PyTorch Database: SQL Server, PostgreSQL, MongoDB, Redis, Neo4j WORK EXPERIENCE

M&T Bank USA

AI/ML Engineer September 2024 - Present

Engineered and deployed large language model (LLM) solutions using LLaMA, GPT-4, and Claude, improving customer query response time by 40% through automated banking support systems.

Developed retrieval-augmented generation (RAG) pipelines with LangChain and LlamaIndex, enabling accurate extraction of financial insights from structured and unstructured data, reducing manual report generation time by 50%.

Orchestrated generative adversarial networks (GANs) for synthetic financial data creation to enhance predictive modeling, increasing model robustness on rare transaction scenarios by 30%.

Optimized Natural Language Processing (NLP) pipelines for named entity recognition (NER) using Python and TensorFlow, improving fraud detection and compliance monitoring coverage by 25%.

Built predictive credit risk models using XGBoost, achieving 20% higher accuracy in early warning scoring for delinquent accounts.

Designed scalable AI/ML pipelines and automated CI/CD workflows using MLOps practices on Google Cloud Platform (Vertex AI, Cloud Build, and Kubeflow) for model training, deployment, and monitoring, reducing release time from weeks to days.

Integrated real-time caching with Redis for LLM-driven customer interaction services, improving system response time by 35%.

Formulated interactive dashboards and reports with Power BI (DAX, Power Query) to visualize model performance, customer sentiment, and operational KPIs, enabling data-driven decision-making for business teams.

Collaborated with cross-functional teams to translate business requirements into ML solutions, achieving a 15% increase in workflow efficiency in internal banking processes.

LTIMindtree India

Machine Learning Engineer August 2020 – July 2023

Established predictive models using Decision Trees, Random Forests, and Naive Bayes to identify high-risk customers, improving early detection accuracy by 28%.

Improved deep learning solutions with Convolutional Neural Networks (CNN) and BERT for image recognition and natural language processing (NLP) tasks, enhancing document classification and sentiment analysis performance by 35%.

Performed exploratory data analysis (EDA) and hypothesis testing using Python, NumPy, Pandas, and Excel, uncovering actionable insights that informed business strategy and reduced operational costs by 15%.

Implemented end-to-end ML pipelines using Scikit-learn, Keras, and PyTorch, automating model training and evaluation, which reduced development cycle time by 25%.

Designed cohort analysis dashboards with Tableau for customer segmentation, driving targeted marketing campaigns and increasing engagement by 20%.

Incorporated cloud-based data services using AWS Kinesis and Athena for real-time data streaming and query processing, improving data availability for ML workflows.

Created Natural Language Processing (NLP) pipelines using NLTK and BERT for text classification and named entity recognition, improving automated report generation accuracy by 22%.

CERTIFICATIONS

AWS Cloud Foundation Data Analytics with Python (NPTEL) Python 101 for Data Science

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