Surendra Sagi

• Designed and deployed Retrieval-Augmented Generation (RAG) pipelines using OpenAI GPT-4, LangChain, and FAISS, delivering real-time insights from structured and unstructured datasets.
• Designed, developed, and deployed end-to-end machine learning and deep learning models using Python, TensorFlow, PyTorch, and XGBoost for classification and clustering problems.
• Fine-tuned LLMs using Hugging Face Transformers and PyTorch, improving model precision and reducing manual response time by 35%.
• Designed and deployed NLP pipelines for text classification, sentiment analysis, and summarization using Hugging Face Transformers, improving text understanding accuracy by 30%
• Automated ETL workflows using PySpark, Pandas, and NumPy, transforming multi-source data for ML model consumption and analytics.
• Designed and trained Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) using Keras and TensorFlow, achieving >90% accuracy on image and sequence classification tasks.
• Built LLM-powered data retrieval pipelines using LangChain, enabling contextual insights from enterprise data stored in S3 and Redshift via Bedrock API orchestration.
• Built and deployed object detection and image segmentation models using OpenCV, Keras, and TensorFlow, enabling automated defect detection in manufacturing images
• Built scalable and modular ML pipelines leveraging SageMaker Processing, Training, and Model Registry for automated retraining and deployment.
• Built metadata-driven ETL frameworks in Python using configuration tables in Databricks SQL, reducing manual maintenance and enabling reuse across 100+ data sources.
• Collaborated with business stakeholders through Dataiku dashboards and insights, providing explainable model outputs and actionable recommendations.
• Developed modular MLOps pipelines on AWS for model training, validation, deployment, and monitoring using CI/CD workflows, Docker, and SageMaker endpoints.
• Built streamlined MLOps pipelines connecting LangChain-based RAG systems with AWS Bedrock, Glue, and Step Functions for production-ready AI integration.
• Built interactive analytics models and dashboards in Tableau, Power BI, and Databricks SQL, enabling self-service BI and operational insights.
• Collaborated with ML, DevOps, and compliance teams in an Agile environment (JIRA), delivering scalable AI-first solutions within strict enterprise and security standards.
• Optimized model performance through hyperparameter tuning, feature engineering, and GPU acceleration, reducing training time and improving accuracy.

Environment: Python, PySpark, Pandas, NumPy, Delta Lake, Azure Databricks, NLP, Event Hubs, Delta Live Tables, Unity Catalog, XGBoost, Power BI, SQL, Node.js, Express.js, React, Redux, GraphQL, Hugging Face Transformers, PyTorch, LangChain, GPT-4, FAISS, Docker, Kubernetes, Terraform, Jenkins, Azure DevOps, Great Expectations, Boto3, AWS CloudFormation, Celery, Redi, AWS Sagemaker, Keras.

• Designed front end and backend of the application using Python on Django Web Framework and AngularJS developed consumer-based features and applications using Python and Django in test driven Development and pair-based programming.
• Designed, trained, and deployed end-to-end machine learning models in Python using AWS SageMaker, optimizing model accuracy and inference latency for production workloads.
• Developing dynamic web pages using HTML5, CSS3, Bootstrap, SASS and JavaScript.
• Working with Python for data manipulation, wrangling, and analysis using libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib.
• AWS services such as Amazon EMR, Redshift, and S3 were assessed from end to end in terms of architecture and execution.
• Developed web applications and RESTful web services and APIs using Python Flask, Django, Pyramid, and PHP.
• Led a cross-functional team in migrating legacy systems to the cloud, focusing on Azure Databricks for its scalability and Python-friendly environment.
• Responsible in Working with various Python integrated development environments like PyCharm, Idle.
• Automated the existing scripts for performance calculations using NumPy and SQL Alchemy.
• Develop the scripts using Perl, Python, Unix and SQL.
• Involved in Unit testing and Integration testing of the code using PyTest.
• Writing a migration script from PostgreSQL to MongoDB with Python, using Gevent, pyscopg2python library, Postgres Cursors, and mongo Bulk insert.
• Implemented AI-driven ETL scripts in Python to automate data extraction, transformation, and loading, increasing efficiency and accuracy.
• Actively pursued and applied the latest advancements in Azure Databricks and Python libraries, fostering a culture of continuous improvement within the team.
• Used AWS including deploying new server instances through automation with Kubernetes and Jenkins.
• Developed using Git and Jira for code tracking and reviewing process.
• Used Python and Django creating graphics, XML processing, data exchange and business logic implementation with Spiff workflow development.
• Part of the team implementing REST API in Python using micro-framework like Flask with SQL Alchemy in the backend for management of data center resources.
• Developed the ETL scripts in Python to get data from one database table and insert, update the resultant data to another database table.

Environment: Python 3.11, Django 4X, REST API, MySQL, Linux, CI/CD, GitHub, PyCharm, AWS, Jenkins Python, AWS, EC2, EBS, S3, VPC, PyCharm, Selenium IDE, jQuery, HTML, CSS, JavaScript, Ajax, Web Services, Pandas, JSON, Angular.js, Bootstrap, Jinja, Flask, MongoDB, AWS SageMaker.

• Designed and maintained ETL pipelines using Hadoop ecosystem tools — including Hive, Pig, Sqoop, and MapReduce — to process and transform multi-terabyte clinical and patient data.
• Developed Spark (Scala and PySpark) jobs for distributed data processing, significantly improving data throughput and reducing batch latency by 40%.
• Created and tuned Hive tables and UDFs for ad-hoc analytics, supporting partitioned and bucketed datasets optimized for downstream BI workloads.
• Integrated NoSQL databases (MongoDB, HBase) with Hadoop and Spark for unstructured data ingestion and near-real-time analytics.
• Optimized Pig workflows and Hive queries, improving query execution times through partition pruning, compression, and parallel processing.
• Automated data ingestion from relational systems (Oracle 11g, SQL Server, Netezza) into HDFS using Sqoop and shell scripts, ensuring incremental and full-load consistency.
• Designed PL/SQL procedures and stored packages to generate operational and customer status reports for healthcare clients.
• Contributed to data warehouse design and SSAS cube development, enhancing multidimensional reporting and OLAP performance through partition and aggregation strategies.
• Supported production Ab Initio jobs, resolving ETL failures, optimizing data dependencies, and ensuring SLA adherence.
• Collaborated on cloud migration pilots to AWS, using S3, EMR, and Redshift to modernize data storage and analytics infrastructure for scalability and cost efficiency.

Environment: Hadoop Ecosystem (Hive, Pig, Sqoop, MapReduce), Spark (Scala, PySpark), Oracle 11g, PL/SQL, SQL Server, Netezza, MongoDB, HBase, Ab Initio, AWS (S3, EMR, Redshift), Power BI, Tableau, SSAS, Shell Scripting, Linux, Git, JIRA

• Designed and developed 50+ Tableau and Power BI dashboards to track KPIs across sales, marketing, and operations, improving executive decision-making and performance visibility.
• Performed data mining and analysis using SQL, Excel, and Python, driving actionable insights for customer segmentation and targeted marketing campaigns.
• Automated data extraction and cleansing processes using Python scripts and shell automation, reducing manual reporting time by over 60%.
• Created and optimized dimensional data models (Star and Snowflake schemas) to support scalable reporting and analytics.
• Built Excel VBA macros and advanced formulas to automate recurring reporting tasks and data reconciliations across departments.
• Developed interactive Tableau dashboards with parameters, trend analysis, and forecasting models to identify seasonal sales patterns and improve revenue predictions.
• Integrated data from multiple SQL sources, CRM systems, and Google Analytics for consolidated customer behavior and campaign performance analysis.
• Defined and implemented custom KPIs and DAX measures in Power BI, enabling teams to monitor conversion rates, churn, and product performance in real time.
• Collaborated with business users to document reporting requirements, validate data accuracy, and enhance visualization usability through intuitive designs and tooltips.
• Conducted A/B testing and statistical analysis in R and Python, providing insights into the effectiveness of marketing and promotional strategies.

Environment: SQL Server, Tableau, Power BI, Excel (VBA, Macros), Python 3.x, R, Shell Scripting, Google Analytics, CRM Systems, Star & Snowflake Schema Modeling, SSRS, Power Query, DAX