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Senior AI/ML Engineer and Data Scientist with 11+ years of experience building production-grade Machine Learning, Deep Learning, and Generative AI/LLM solutions across Healthcare, Insurance, Financial Services, Telecommunications, and Energy. Strong expertise in RAG, agentic AI, prompt engineering, and enterprise conversational AI. I design scalable end-to-end ML/LLM systems—from data pipelines and feature engineering to model deployment, monitoring, and MLOps/LLMOps—using Python, PySpark/Spark, AWS/Azure/GCP, MLflow, Kubernetes, and CI/CD. I collaborate across cross-functional teams to deliver secure, reliable, and business-aligned AI platforms.…Senior AI/ML Engineer and Data Scientist with 11+ years of experience building production-grade Machine Learning, Deep Learning, and Generative AI/LLM solutions across Healthcare, Insurance, Financial Services, Telecommunications, and Energy. Strong expertise in RAG, agentic AI, prompt engineering, and enterprise conversational AI. I design scalable end-to-end ML/LLM systems—from data pipelines and feature engineering to model deployment, monitoring, and MLOps/LLMOps—using Python, PySpark/Spark, AWS/Azure/GCP, MLflow, Kubernetes, and CI/CD. I collaborate across cross-functional teams to deliver secure, reliable, and business-aligned AI platforms.

Sandeep Reddy Boggula

Data Scientist, AI Engineer, AI Developer, +6

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Senior AI/ML Engineer and Data Scientist with 11+ years of experience building production-grade Machine Learning, Deep Learning, and Generative AI/LLM solutions across Healthcare, Insurance, Financial Services, Telecommunications, and Energy. Strong expertise in RAG, agentic AI, prompt engineering, and enterprise conversational AI. I design scalable end-to-end ML/LLM systems—from data pipelines and feature engineering to model deployment, monitoring, and MLOps/LLMOps—using Python, PySpark/Spark, AWS/Azure/GCP, MLflow, Kubernetes, and CI/CD. I collaborate across cross-functional teams to deliver secure, reliable, and business-aligned AI platforms.…Senior AI/ML Engineer and Data Scientist with 11+ years of experience building production-grade Machine Learning, Deep Learning, and Generative AI/LLM solutions across Healthcare, Insurance, Financial Services, Telecommunications, and Energy. Strong expertise in RAG, agentic AI, prompt engineering, and enterprise conversational AI. I design scalable end-to-end ML/LLM systems—from data pipelines and feature engineering to model deployment, monitoring, and MLOps/LLMOps—using Python, PySpark/Spark, AWS/Azure/GCP, MLflow, Kubernetes, and CI/CD. I collaborate across cross-functional teams to deliver secure, reliable, and business-aligned AI platforms.

Available to hire

Senior AI/ML Engineer and Data Scientist with 11+ years of experience building production-grade Machine Learning, Deep Learning, and Generative AI/LLM solutions across Healthcare, Insurance, Financial Services, Telecommunications, and Energy. Strong expertise in RAG, agentic AI, prompt engineering, and enterprise conversational AI.

I design scalable end-to-end ML/LLM systems—from data pipelines and feature engineering to model deployment, monitoring, and MLOps/LLMOps—using Python, PySpark/Spark, AWS/Azure/GCP, MLflow, Kubernetes, and CI/CD. I collaborate across cross-functional teams to deliver secure, reliable, and business-aligned AI platforms.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

Data Scientist (Gen AI) at Verizon, Dallas, TX

June 1, 2023 - Present

Designed, developed, and deployed enterprise-scale Generative AI and Agentic AI solutions using Python and LLM orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex). Built RAG pipelines using vector databases (Pinecone/FAISS/Chroma) with semantic search and prompt engineering. Delivered end-to-end ML/DL solutions for analytics use cases such as churn prediction, anomaly detection, predictive maintenance, forecasting, and recommendations. Engineered distributed ETL/ELT pipelines using PySpark/Apache Spark/Databricks with Airflow and Kafka, and implemented Delta Lake-based data processing for large-scale structured and unstructured datasets. Deployed secure cloud-native AI platforms on AWS/Azure/GCP (SageMaker/Bedrock/Vertex AI/Azure OpenAI/Azure ML/EMR/Dataproc), exposing models via FastAPI/REST APIs. Implemented MLOps/LLMOps using MLflow, Docker, Kubernetes, Jenkins, GitHub Actions, Terraform, and CI/CD with monitoring/observability (Splunk, Prometheus, Grafana)

Data Scientist (Gen AI) at Verizon

June 1, 2023 - Present

Data Scientist (Gen AI) at Verizon (Dallas, TX). Spearheaded the design and deployment of agentic AI systems using LangGraph, CrewAI, and AutoGen, integrating LlamaIndex with Pinecone vector stores to enable scalable Retrieval-Augmented Generation (RAG) across telecom knowledge bases and operations. Built end-to-end ML/GenAI pipelines for predictive analytics, network anomaly detection, and churn reduction, deployed on AWS/Azure with monitoring for drift and bias. Applied advanced NLP techniques (tokenization, lemmatization, sentiment analysis, topic modeling, NER) to extract actionable insights from large-scale telecom data for customer experience and operational intelligence. Developed DL models (CNN, RNN, LSTM, Transformers) using TensorFlow, Keras, and PyTorch to boost recommendation accuracy, classification, and demand forecasting. Implemented MLOps with Docker, Kubernetes, Jenkins, and MLflow for CI/CD and model retraining; created executive dashboards in Power BI/Tableau. Built

AI/ML Engineer at New York Statewide Financial System, Dallas, TX

March 1, 2020 - April 1, 2023

Designed and deployed enterprise ML/AI and predictive analytics solutions for forecasting, fraud detection, and risk assessment. Built end-to-end ML pipelines including data acquisition, EDA, feature engineering, training, hyperparameter tuning, deployment, monitoring, and continuous improvement. Developed scalable ETL/ELT pipelines with PySpark/Apache Spark, Databricks, Airflow, and Hadoop to support analytics and AI workloads. Implemented supervised/unsupervised models (regression, classification, clustering, gradient boosting/XGBoost, and time-series forecasting) with statistical analysis, hypothesis testing, and A/B testing. Delivered NLP solutions using Transformer-based models (BERT) for document classification, sentiment analysis, entity extraction, intelligent search, and summarization. Created GenAI/RAG proof-of-concepts using OpenAI GPT, LangChain, vector databases, and prompt engineering. Exposed services via FastAPI/Flask APIs and established MLOps with MLflow, Docker, Kube

AI/ML Engineer at New York Statewide Financial System

March 1, 2020 - April 1, 2023

AI/ML Engineer at New York Statewide Financial System (Dallas, TX). Designed and maintained scalable data pipelines ingesting multi-source financial datasets for fraud detection, credit risk, and portfolio analytics. Leveraged AWS (S3, EMR, Lambda, SageMaker), Azure Data Factory, and Apache Spark 3.x to process large-scale financial data while ensuring SOX, PCI DSS, and GDPR compliance. Built deep learning architectures for anomaly detection, credit default prediction, and document parsing; performed hyperparameter tuning and transfer learning to boost accuracy. Implemented regression (Linear, Ridge, Lasso, ElasticNet) and classification (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM) models for fraud, credit scoring, and churn. Used Autoencoders/VAEs for anomaly detection, GANs for synthetic data, and GNNs for interbank networks. Implemented time-series forecasting (ARIMA, Prophet, LSTM, GRU) for stock prices and revenue; employed Monte Carlo VaR and stress testing. Execu

Data Scientist / Data Analyst at Homesite Insurance, Boston, MA

August 1, 2016 - February 1, 2020

Built end-to-end ML and predictive analytics solutions for insurance risk assessment, claims prediction, fraud detection, customer segmentation, and policy renewal forecasting. Engineered scalable ETL/ELT pipelines using PySpark/Apache Spark, Hadoop, Hive, Airflow, and Databricks. Developed predictive models (regression/classification/clustering, gradient boosting, XGBoost, and time-series forecasting) and applied EDA, feature engineering/selection, and hypothesis testing/A-B testing. Delivered NLP solutions using spaCy, NLTK, and Transformer/BERT architectures for classification, sentiment analysis, NER, and document extraction. Built cloud-native training/deployment workflows using AWS/Azure/GCP (SageMaker, Azure ML, S3, EMR, BigQuery, Dataproc). Implemented MLOps with MLflow, Docker, Kubernetes, Jenkins, Git/GitHub, and CI/CD, including monitoring and production support. Created REST APIs with FastAPI/Flask and built dashboards/reports using Power BI/Tableau/SQL and visualization li

Data Scientist / Data Analyst at Homesite Insurance

August 1, 2016 - February 1, 2020

Developed and deployed ML and predictive analytics for insurance risk assessment, claims prediction, fraud detection, customer segmentation, and policy renewal forecasting. Built ETL/ELT pipelines with PySpark, Spark, SQL, Hadoop/Hive, Airflow, and Databricks for large-scale insurance data. Implemented predictive models (regression/classification/clustering, gradient boosting, XGBoost, time series) with statistical analysis, EDA, feature engineering/selection, hypothesis testing, and A/B testing. Produced NLP solutions for document classification, sentiment analysis, NER, and intelligent extraction using spaCy, NLTK, and Hugging Face/BERT/Transformer models. Deployed cloud-native training/inference on AWS/Azure/GCP (SageMaker, Azure ML, S3, EMR, BigQuery, Dataproc). Established MLOps with MLflow, Docker, Kubernetes, Jenkins/GitHub, and CI/CD; created secure REST APIs (FastAPI/Flask) and executive dashboards using Power BI/Tableau and Python visualization libraries. Worked with cross-fu

Data Scientist/Data Analyst at Homesite Insurance

August 1, 2016 - February 1, 2020

Data Scientist/Data Analyst at Homesite Insurance (Boston, MA). Developed predictive models for policy sales, claim volumes, and retention; applied ARIMA/Prophet for time-series forecasting of claims and underwriting planning. Built CNN-based image classification for document verification, claims processing, and damage assessment with 87%+ accuracy. Used ML algorithms (Decision Trees, Random Forest, Gradient Boosting, SVM, clustering) for fraud detection, customer segmentation, and churn analysis. Developed NLP pipelines (TF-IDF, Word2Vec, NLTK) for claims notes, emails, and underwriting docs to improve risk evaluation. Created dashboards in Tableau/Power BI for claims trends and underwriting metrics. Implemented end-to-end MLOps with AWS S3/Lambda/MLflow/Airflow; explored Generative AI via LangChain/OpenAI for automated claim responses, policy recommendations, and text summarization.

Data Engineer at Chevron Corporation, Houston, TX

February 1, 2014 - July 1, 2016

Developed AI/ML solutions to support predictive maintenance, equipment failure prediction, and operational analytics across energy operations. Built distributed ETL/ELT pipelines using PySpark, Apache Spark, Hadoop, Hive, and Airflow for sensor and operational datasets. Implemented regression/classification/clustering and time-series forecasting models; applied statistical analysis, feature engineering, and hypothesis testing to improve operational decisions. Developed computer vision solutions using OpenCV and deep learning/CNNs for automated visual inspection and defect detection. Delivered NLP capabilities using spaCy/NTLK for document classification and information extraction from engineering reports. Implemented cloud-based analytics and ML using AWS (EC2, S3, EMR, Lambda, SageMaker). Built dashboards and executive reports (Power BI/Tableau) and implemented governance/data quality/metadata management and automated validation. Exposed predictive analytics via FastAPI/Flask and opti

Data Engineer at Chevron Corporation

February 1, 2014 - July 1, 2016

Data Engineer at Chevron Corporation (Houston, TX). Applied ML techniques to optimize production forecasts, well performance, and predictive maintenance. Built real-time ETL pipelines for SCADA/IoT data into AWS Redshift using S3, EC2, Glue, and Data Pipeline. Created dashboards with Tableau for reservoir performance and drilling KPIs. Performed NLP on maintenance reports to identify operational risks; integrated SOAP-based Web Services for external systems; designed star/snowflake schemas and SCDs for energy data warehouses. Implemented real-time streaming with Kafka, Spark Streaming, and Flink; led data modeling, big data processing with Hadoop/Spark/Hive; developed OCR-assisted document processing for asset integrity. Collaborated with cross-functional teams in Agile/Scrum to deliver upstream/downstream analytics.