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Hi, I'm Sai Kumar Naik Mude, a Gen AI Engineer and Data Scientist with 10 years of experience delivering production-grade AI and data solutions across healthcare, banking, and retail. I specialize in end-to-end ML systems, spanning data ingestion, feature engineering, modeling, deployment, monitoring, and governance. I have a strong background in predictive analytics, classical ML, deep learning, and NLP, with hands-on experience building scalable pipelines and deploying models on AWS, Azure, and GCP. I focus on responsible AI, explainability, bias mitigation, and regulatory compliance, while collaborating closely with product and engineering teams to translate requirements into measurable, production-ready outcomes.…Hi, I'm Sai Kumar Naik Mude, a Gen AI Engineer and Data Scientist with 10 years of experience delivering production-grade AI and data solutions across healthcare, banking, and retail. I specialize in end-to-end ML systems, spanning data ingestion, feature engineering, modeling, deployment, monitoring, and governance. I have a strong background in predictive analytics, classical ML, deep learning, and NLP, with hands-on experience building scalable pipelines and deploying models on AWS, Azure, and GCP. I focus on responsible AI, explainability, bias mitigation, and regulatory compliance, while collaborating closely with product and engineering teams to translate requirements into measurable, production-ready outcomes.

Sai Kumar Naik Mude

Data Scientist, AI Engineer, Data Analyst, +4

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Hi, I'm Sai Kumar Naik Mude, a Gen AI Engineer and Data Scientist with 10 years of experience delivering production-grade AI and data solutions across healthcare, banking, and retail. I specialize in end-to-end ML systems, spanning data ingestion, feature engineering, modeling, deployment, monitoring, and governance. I have a strong background in predictive analytics, classical ML, deep learning, and NLP, with hands-on experience building scalable pipelines and deploying models on AWS, Azure, and GCP. I focus on responsible AI, explainability, bias mitigation, and regulatory compliance, while collaborating closely with product and engineering teams to translate requirements into measurable, production-ready outcomes.…Hi, I'm Sai Kumar Naik Mude, a Gen AI Engineer and Data Scientist with 10 years of experience delivering production-grade AI and data solutions across healthcare, banking, and retail. I specialize in end-to-end ML systems, spanning data ingestion, feature engineering, modeling, deployment, monitoring, and governance. I have a strong background in predictive analytics, classical ML, deep learning, and NLP, with hands-on experience building scalable pipelines and deploying models on AWS, Azure, and GCP. I focus on responsible AI, explainability, bias mitigation, and regulatory compliance, while collaborating closely with product and engineering teams to translate requirements into measurable, production-ready outcomes.

Available to hire

Hi, I’m Sai Kumar Naik Mude, a Gen AI Engineer and Data Scientist with 10 years of experience delivering production-grade AI and data solutions across healthcare, banking, and retail. I specialize in end-to-end ML systems, spanning data ingestion, feature engineering, modeling, deployment, monitoring, and governance.

I have a strong background in predictive analytics, classical ML, deep learning, and NLP, with hands-on experience building scalable pipelines and deploying models on AWS, Azure, and GCP. I focus on responsible AI, explainability, bias mitigation, and regulatory compliance, while collaborating closely with product and engineering teams to translate requirements into measurable, production-ready outcomes.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Gen AI Engineer / Data Scientist at Cigna

March 1, 2024 - November 7, 2025

Designed and deployed AI-driven patient risk models using Python, TensorFlow, and AWS SageMaker, improving early-risk detection and treatment accuracy by 25%. Enhanced transparency with SHAP and LIME through SageMaker Model Monitor. Built treatment recommendation systems using Keras and TensorFlow to optimize therapy decisions and patient outcomes. Developed NLP pipelines with SpaCy, Hugging Face Transformers, and Amazon Comprehend Medical for document classification and entity extraction. Created LLM-based assistants with Amazon Bedrock and LangChain to summarize clinical reports and patient histories. Built RAG pipelines with LangChain, FAISS, and Amazon OpenSearch for medical literature retrieval. Implemented real-time claims ingestion and fraud detection pipelines with AWS Kinesis, Lambda, and Glue, reducing manual review time by 35%. Automated retraining workflows with AWS Step Functions, Lambda, and Docker, improving model deployment speed. Developed predictive analytics workflow

Gen AI / Data Scientist at Brown Brothers Harriman (BBH)

February 1, 2024 - February 1, 2024

Designed and deployed credit risk scoring models with Python, Scikit-learn, and Azure ML, improving loan approval accuracy and reducing false declines by 20%. Built real-time fraud detection using Isolation Forest, Autoencoders, and PyTorch on Azure Databricks. Developed LLM-powered NLP pipelines with LangChain, Hugging Face Transformers, and Azure Cognitive Services to extract insights from regulatory filings and financial reports. Implemented RAG frameworks leveraging Azure OpenAI and FAISS to automate regulatory summarization and risk insights. Built churn prediction models with XGBoost, LightGBM, and Azure Synapse Analytics, increasing retention precision by 18%. Created AML knowledge graphs with GraphFrames and Neo4j in Azure Databricks to reveal hidden links. Automated CI/CD pipelines for ML/GenAI models with Docker, Terraform, and Azure DevOps. Ensured GDPR and Basel II compliance via SHAP and LIME fairness checks and Azure Responsible AI Dashboard. Delivered real-time risk dash

AI/ML Engineer / Data Scientist at Starbucks

August 1, 2021 - August 1, 2021

Developed and deployed ML models for recommendations, demand forecasting, sentiment analysis, and fraud detection using Python, TensorFlow, PyTorch, Scikit-learn, XGBoost, and CatBoost on AWS SageMaker. Designed data-driven supply chain optimization across 1,500+ stores. Built automated ETL workflows with AWS Glue, Lambda, and Redshift to streamline data ingestion and model retraining. Created end-to-end ML pipelines with Docker, AWS Step Functions, and SageMaker Pipelines, accelerating deployment by 40%. Built real-time analytics dashboards in Amazon QuickSight and Redshift to track store performance and customer trends. Implemented unsupervised learning (KMeans, DBSCAN) for customer segmentation and targeted marketing. Maintained governance and security with IAM, KMS, and CloudTrail to ensure security and compliance.

Data Scientist at Yana Software Private Limited

September 1, 2019 - September 1, 2019

Designed and deployed predictive classification models using Python, Scikit-learn, and Google Cloud Vertex AI, improving customer analytics across enterprise applications. Built real-time data processing pipelines with Apache Beam and Google Cloud Dataflow for low-latency operations. Developed NLP-driven document classification systems using TensorFlow, Keras, and Google Cloud AI Platform. Orchestrated end-to-end ETL workflows using Python, SQL, and BigQuery, supporting scalable reporting and data warehousing. Implemented recommendation engines using collaborative filtering to enhance personalization in enterprise apps. Engineered real-time dashboards and analytics using Looker and BigQuery for KPI visibility. Deployed fraud detection and anomaly detection models with Isolation Forest, PyTorch, and Google Cloud ML Engine. Automated CI/CD pipelines with Docker, Jenkins, and Google Kubernetes Engine (GKE).

NLP Engineer at Aufait Technologies Pvt. Ltd

June 1, 2017 - June 1, 2017

Designed and implemented predictive analytics and statistical models using Python, R, TensorFlow, and scikit-learn. Developed and optimized SQL Server databases, stored procedures, and triggers for high-performance data retrieval. Built and maintained interactive dashboards in Power BI, Excel, and Jupyter Notebooks. Created neural network prototypes with TensorFlow and Keras to explore deep learning for structured data. Applied algorithms including Random Forest, XGBoost, and Logistic Regression for predictive modeling and benchmarking. Automated ETL and data-prep workflows with Python, SSIS, and Excel macros. Conducted EDA with Pandas, NumPy, and Matplotlib to validate data quality and feature selection. Collaborated with business and engineering teams to ensure analytics outputs were actionable and measurable.

Gen AI Engineer at Cigna

March 1, 2024 - November 25, 2025

Designed and deployed AI-driven patient risk models, improving early-risk detection and treatment accuracy by 25%. Implemented explainable AI with SHAP and LIME via SageMaker Model Monitor. Built NLP pipelines for document classification and entity extraction. Created LLM-based assistants with Amazon Bedrock and LangChain to summarize clinical reports and patient histories. Built RAG pipelines using LangChain, FAISS, and Amazon OpenSearch for medical literature retrieval. Established real-time claims ingestion and fraud detection pipelines with AWS Kinesis, Lambda, and Glue, reducing manual review time by 35%. Implemented anomaly detection with Isolation Forest and Scikit-learn via AWS Batch, automated retraining with Step Functions and Docker, and deployed population health forecasting models with PyTorch on SageMaker. Set up continuous model monitoring with Evidently AI and CloudWatch to track drift and stability. Ensured HIPAA-compliant data security using AWS IAM and KMS.

Gen AI Engineer / Data Scientist at Brown Brothers Harriman (BBH)

February 1, 2024 - February 1, 2024

Designed and deployed credit risk scoring models improving loan approval accuracy and reducing false declines by 20%. Built fraud detection using Isolation Forest, Autoencoders, and PyTorch on Azure Databricks for real-time anomaly detection. Developed LLM-powered NLP pipelines with LangChain, Hugging Face, and Azure Cognitive Services to extract insights from regulatory filings and financial reports. Implemented RAG frameworks leveraging Azure OpenAI and FAISS to automate regulatory summarization and risk insights generation. Built churn prediction models with XGBoost, LightGBM, and Azure Synapse Analytics, increasing retention precision. Developed GNN-based AML models with PyTorch Geometric and Neo4j. Designed ETL workflows with Python, Azure Data Factory and SQL. Automated CI/CD pipelines for ML and GenAI models using Docker, Terraform, and Azure DevOps. Applied SHAP and LIME for bias and fairness and maintained GDPR/Basel II compliance. Created knowledge-graph AML systems integrati

Python Developer at Aufait Technologies Pvt. Ltd

June 1, 2017 - June 1, 2017

Designed predictive analytics and statistical models using Python, R, TensorFlow, and scikit-learn to uncover key business insights. Developed optimized SQL Server databases, stored procedures, and triggers for high-performance data retrieval. Built dashboards in Power BI, Excel, and Jupyter to visualize KPIs. Created neural network prototypes with TensorFlow and Keras. Applied algorithms including Random Forest, XGBoost, and Logistic Regression for predictive modeling. Automated ETL and data-preparation workflows with Python scripts, SSIS, and Excel macros. Performed EDA with Pandas, NumPy, and Matplotlib to guide feature selection. Collaborated with business and engineering teams to ensure data-driven recommendations were actionable.

Gen AI / Data Scientist at Brown Brothers Harriman (BBH)

September 1, 2021 - February 1, 2024

Designed and deployed credit risk scoring models using Python, Scikit-learn, and Azure Machine Learning, improving loan approval accuracy and reducing false declines by 20%. Built fraud detection systems with Isolation Forest, Autoencoders, and PyTorch on Azure Databricks for real-time anomaly detection. Implemented RAG frameworks with Azure OpenAI and FAISS to automate regulatory summarization and risk insights generation; developed churn prediction models; created a knowledge graph AML system using GraphFrames, LangChain, and Azure Cognitive Search; established end-to-end model governance with MLflow and Azure Model Registry.

AI/ML Engineer / Data Scientist at Starbucks

January 1, 2020 - August 1, 2021

Developed and deployed ML models for recommendation systems, demand forecasting, sentiment analysis, and fraud detection using Python, TensorFlow, PyTorch, Scikit-learn, XGBoost, and CatBoost on Amazon SageMaker. Designed data-driven supply chain optimization across 1,500+ stores, improving inventory accuracy and operational efficiency. Built automated ETL pipelines with AWS Glue, Lambda, and Redshift; created end-to-end ML pipelines with Docker, AWS Step Functions, and SageMaker Pipelines. Developed real-time dashboards in QuickSight and Redshift; implemented unsupervised learning (KMeans, DBSCAN) for customer segmentation; ensured data governance and compliance (SOC 2, GDPR).

Data Scientist at Yana Software Private Limited

July 1, 2017 - September 1, 2019

Designed and deployed predictive classification models using Python, Scikit-learn, and Google Vertex AI; built real-time data processing pipelines with Apache Beam and Google Cloud Dataflow; developed NLP document classification systems with TensorFlow and Keras; orchestrated end-to-end ETL with Python, SQL, and BigQuery; implemented recommendation engines; engineered real-time dashboards in Looker and BigQuery; deployed fraud detection and anomaly detection models with Isolation Forest and PyTorch; automated CI/CD pipelines with Docker, Jenkins, and GKE.

Python Developer at Aufait Technologies Pvt. Ltd

December 1, 2015 - June 1, 2017

Designed predictive analytics and statistical models using Python, R, TensorFlow, and Scikit-learn to uncover key business insights from enterprise data. Developed and optimized SQL Server databases, stored procedures, and triggers for high-performance data retrieval. Built and maintained interactive dashboards in Power BI, Excel, and Jupyter; created neural network prototypes with TensorFlow/Keras; automated ETL and data-preparation workflows with Python scripts and SSIS; applied ML algorithms including Random Forest, XGBoost, and Logistic Regression; collaborated with business and engineering teams to ensure data-driven recommendations were actionable.

AI/ML Engineer / Data Scientist at Brown Brothers Harriman (BBH)

September 1, 2021 - February 1, 2024

Designed and deployed credit risk scoring models, improving approval accuracy and reducing false declines ~20%. Built fraud and anomaly detection models using Isolation Forest, Autoencoders, and PyTorch on Azure Databricks. Developed NLP pipelines for regulatory documents and implemented LLM-powered summarization/analysis workflows using Azure OpenAI and LangChain. Built FAISS-powered RAG for regulatory insights; developed churn models (XGBoost/LightGBM); graph AML models with PyTorch Geometric and Neo4j. Implemented ETL pipelines with Python/SQL and Azure Data Factory; automated CI/CD for ML with Docker, Terraform, and Azure DevOps. Applied explainability/fairness tools (SHAP, LIME) for GDPR/Basel II compliance; built governance dashboards with Power BI; established MLflow/DVC-based monitoring.