ML Engineer - NLP, LLMs, MLOps Expert

Lakshmi Vinitha Pagadala ML Engineer

Denton, TX +1-940-***-**** ************************@*****.*** LinkedIn SUMMARY

Machine Learning Engineer with 6 years of experience designing and deploying production-grade ML and LLM-powered systems across healthcare and enterprise domains. Expertise in end-to-end model development, scalable NLP pipelines, and MLOps, with hands-on experience in Azure-based deployments, CI/CD automation, and model monitoring. Proven track record of improving model accuracy (up to 92%), reducing system latency, and building LLM-driven solutions using transformer architectures, embeddings, and retrieval-based systems. Strong focus on production reliability, explainability, and data-driven decision systems. SKILLS

Programming & Data: Python (Pandas, NumPy), SQL (PostgreSQL, BigQuery) Machine Learning: Scikit-learn, XGBoost, LightGBM, Supervised & Unsupervised Learning, Feature Engineering, Model Evaluation, Hyperparameter Tuning

Deep Learning & NLP: TensorFlow, PyTorch, Hugging Face Transformers, BERT, GPT-based Models, Text Classification, Summarization

Generative AI / LLMs: LLM Applications, Prompt Engineering, Retrieval-Augmented Generation (RAG), Embeddings, Vector Databases (FAISS), LLM Evaluation & Optimization

MLOps & Deployment: Azure ML, MLflow, Docker, CI/CD (GitHub Actions/Azure DevOps), Model Monitoring, Drift Detection, Automated Retraining Pipelines

Data Engineering: ETL Pipelines, Data Validation, Feature Engineering Pipelines, Large-scale Data Processing Tools & Platforms: Git, Airflow, REST APIs, Automated Testing EXPERIENCE

UnitedHealth Group TX, USA Jan 2025 – Present

ML Engineer

• Built and maintained ML pipelines for patient risk prediction and claims processing on large healthcare datasets, improving model accuracy from 78% to 92% and reducing high-risk cases by 12%.

• Developed LLM-based pipelines for clinical text summarization and classification using transformer models, increasing accuracy by 18% and reducing manual review effort by 40%.

• Implemented retrieval-augmented generation (RAG) workflows using embeddings and FAISS to improve context extraction from clinical notes and patient feedback.

• Exposed models through REST APIs for real-time and batch inference, improving response time and system integration.

• Managed experiments and model versions using MLflow, enabling consistent tracking and faster iteration.

• Set up monitoring pipelines for data and concept drift, triggering retraining to maintain model performance.

• Applied SHAP and LIME to interpret model outputs and support compliance with healthcare standards (HIPAA).

• Worked with engineering teams to deploy models using CI/CD pipelines and Docker, improving deployment reliability. Hexaware Technologies India Jan 2019 - Jun 2023

ML Engineer

• Developed ML models for sales forecasting, churn prediction, customer segmentation, and sentiment analysis, improving forecast accuracy by 18% and increasing retention by 12%.

• Built end-to-end pipelines covering data ingestion, feature engineering, training, and deployment, reducing processing time by 25%.

• Created NLP pipelines using transformer models for sentiment analysis and text classification across enterprise datasets.

• Performed exploratory data analysis on 50K+ records using PCA, hypothesis testing, and correlation analysis to improve feature selection.

• Deployed models using Azure ML with CI/CD and automated testing, supporting scalable delivery across clients.

• Implemented monitoring systems to track model performance and reduce degradation in production.

• Delivered recommendation systems and analytics dashboards, contributing to a 15% increase in ROI.

• Partnered with product and engineering teams to integrate models into APIs and business workflows, improving usability and adoption.

EDUCATION

Master of Science in Computer Science May 2025

University of North Texas, Denton, TX, USA

Bachelor of Science in Computer Science May 2019

Anurag Engineering College, Kodad, Telangana, India

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