Sai Nandyala | Machine Learning Engineer

Hello, This is

Machine Learning Engineer|

About Me

Machine Learning Engineer with around 5 years of experience architecting and deploying production-grade AI & ML solutions across healthcare, financial services, and e-commerce domains.

Proven expertise in statistical modeling, deep learning architectures (CNN, LSTM, Transformers), predictive analytics, and Generative AI (LLMs, RAG). Strong background in end-to-end MLOps—from feature pipelines and experiment tracking with MLflow to containerized deployment on AWS SageMaker. Holds a Master of Science in Data Science from The University of Texas at Arlington.

Work Experience & Education

Machine Learning Engineer

The Cigna Group · Bloomfield, CT

Feb 2025 – Present

Developed predictive models to identify high-risk members using healthcare claims, clinical, and demographic datasets.
Built deep learning classification models using TensorFlow to support patient risk stratification initiatives.
Engineered healthcare-specific features including utilization trends, chronic condition indicators, and treatment history variables.
Implemented explainability techniques (SHAP) to provide transparency into prediction outcomes for healthcare stakeholders.
Established MLflow-based experiment tracking and deployed inference pipelines using AWS SageMaker endpoints with drift monitoring.

PythonTensorFlowAWS SageMakerMLflowPostgreSQLDockerPandasTableau

AI/ML Engineer

Citigroup · New York, NY

Jul 2022 – Nov 2023

Developed machine learning solutions for detecting suspicious financial transactions and fraud patterns.
Processed large volumes of transactional and customer behavior data for model development and validation.
Built feature engineering pipelines to generate transaction frequency, behavioral, and risk-related attributes.
Trained classification models using Scikit-learn and optimized precision, recall, and ROC-AUC metrics.
Integrated trained models into financial risk systems through containerized Docker deployments with automated scoring.

PythonScikit-learnAWSMLflowPostgreSQLDockerPandasPower BI

Junior Machine Learning Engineer

Etsy · Brooklyn, NY

Apr 2020 – Jun 2022

Built recommendation models to improve product discovery and customer engagement across the marketplace platform.
Collected and analyzed user interaction, purchase history, and browsing behavior datasets.
Developed deep learning recommendation models using PyTorch to capture customer preference patterns.
Engineered customer affinity and product similarity features, evaluating ranking and relevance metrics.
Designed scalable batch inference workflows for generating personalized recommendations in production.

PythonPyTorchAWS S3MongoDBDockerNumPyPandasTableau

Master of Science in Data Science

The University of Texas at Arlington · Arlington, TX

Jan 2024 – Jan 2026

Specialized in Machine Learning, Deep Learning Architectures, Statistical Modeling, and Large-Scale Data Systems.
Conducted coursework and project implementations in Generative AI, Retrieval-Augmented Generation (RAG), and Explainable AI (XAI).

Machine LearningDeep LearningNLPStatistical MethodsGenerative AI

Technical Skills

Programming Languages

Python

SQL

Java

C++

Ruby

Machine Learning & Deep Learning

Scikit-learn

TensorFlow

PyTorch

Keras

CNN & LSTM

Transformers

Autoencoders

Generative AI & NLP

LLMs & RAG

Prompt Eng

Vector DBs

BERT & Embeddings

NER & Classification

Data Processing & Orchestration

Pandas

NumPy

Feature Eng

Apache Airflow

ETL Pipelines

Data Validation

Cloud Platforms (AWS)

AWS SageMaker

AWS S3

AWS Lambda

AWS EC2

MLOps Tools & CI/CD

MLflow

Docker

Git

CI/CD Pipelines

Monitoring & Explainability

SHAP & LIME

Drift Detection

Performance Mon.

Responsible AI

Databases

PostgreSQL

MySQL

MongoDB

Visualization

Tableau

Power BI

Matplotlib

Methodologies

ML Lifecycle

A/B Testing

Agile Scrum

Featured Projects & Architectures

Patient Risk Stratification System

Predictive deep learning classification models built with TensorFlow using healthcare claims, clinical, and demographic data. Deployed on AWS SageMaker with MLflow tracking and SHAP explainability.

TensorFlowAWS SageMakerMLflowSHAPPostgreSQL

Financial Fraud & Risk Scoring Engine

Machine learning solution for detecting suspicious financial transactions and fraud patterns. Engineered high-dimensional behavioral attributes with containerized Docker deployment and automated scoring.

Scikit-learnPythonDockerAWSPower BI

Marketplace Recommendation Engine

Deep learning recommendation models using PyTorch to capture customer preference patterns, customer affinity, and product similarity for personalized e-commerce discovery.

PyTorchAWS S3MongoDBDockerTableau

Personalized Fitness Tracker

Data-driven health and workout analytics platform offering personalized exercise insights, trend forecasting, and behavioral tracking routines.

PythonPandasAnalyticsPredictive ML

Job Application Tracker (trackeJA)

Workflow automation and recruitment tracker designed to manage application pipelines, statuses, interview scheduling, and analytical metrics.

Full StackDatabaseWorkflow Automation

Information Theory & Statistical Modeling

Computational statistical analysis and information theory implementations examining entropy, channel capacity, and probabilistic modeling.

PythonNumPyInformation TheoryStatistical Modeling

Certifications & Credentials

AWS Certified Machine Learning Engineer – Associate

Amazon Web Services Training and Certification

Validates production expertise in developing, deploying, scaling, and monitoring ML models on AWS SageMaker, drift detection, and automated ML pipelines.

Verified on CredlyAWS Certification

Google Data Analytics Professional Certificate

Google · Coursera

Comprehensive 8-course specialization covering data preparation, statistical analysis, SQL queries, Tableau dashboards, and R programming.

Credential: QEMKA56TIVB68 Courses Completed

AWS Cloud Technology Consultant Professional Certificate

Amazon Web Services · Coursera

Rigorous 9-course program on translating business and enterprise goals into scalable, reliable AWS cloud solutions and technical architectures.

Credential: VLA8YB8RPQZD9 Courses Completed

AWS Academy Graduate – Cloud Foundations

Amazon Web Services Training and Certification

Foundational proficiency across AWS global infrastructure, core cloud computing services, security compliance, architecture, and pricing.

Verified on CredlyAWS Academy

Master of Science in Data Science

The University of Texas at Arlington

Advanced graduate coursework in Machine Learning, Statistical Modeling, Deep Learning, and Distributed Data Systems (Jan 2024 – Jan 2026).

Location: Arlington, TXStatus: Graduated

Let's Connect

EmailSaiernandyala@gmail.com

Phone(682) 499-0165

LinkedInsaienandyala

GitHubsaieshwarreddy05