Hemanth Ratakonda

1. Developed and deployed AI-powered chatbots using OpenAI's ChatGPT, significantly enhancing customer engagement and automating support workflows. 2. Designed and implemented advanced NLP algorithms, including text summarization models, resulting in more efficient and accurate information retrieval. 3. Utilized Microsoft Azure cloud services to build scalable and low-latency AI solutions, optimizing model performance and deployment speed. 4. Collaborated with cross-functional teams to identify and implement strategic AI use cases, leading to measurable increases in user interaction and satisfaction. 5. Led user testing and feedback initiatives to iteratively improve chatbot responsiveness and summarization precision based on real-world usage. 6. Performed data engineering tasks, including data extraction, transformation, and loading (ETL), ensuring efficient data pipelines and storage solutions. 7. Extracted text from images and PDFs using OCR and machine learning techniques, improving document analysis and data retrieval efficiency.

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1. Write ETL, ELTs using databricks, ADF, delta-lake 2. Create ETL processes to extract images from the CRM and execute AI algorithms to detect and prevent fraudulent activities. 3. Implemented MLOps practices, including continuous monitoring and improving data engineering processe 4. Led and mentored junior data engineers, providing guidance on best practices and development methodo 5. Managed lifecycle elements of ETL development, from robust testing to final deployment.

After navigating the challenges of the real estate market, I regrettably acknowledge that my startup venture in real estate did not achieve the success I had envisioned

. Design and Develop Data Pipelines using snowake, Databricks. . Develop ETLs, ELTs using spark, ADF, NIFI, Devops . Clean data and extract large datasets with more than 500 attributes Evaluate various ML Platforms. . Design and implemented Model ops Architecture . Monitored incoming data analytics requests, executed analytics and eciently distributed results to support strategies. . Tested and validated models for accuracy of predictions in outcomes of interest.

1. Transformed raw data to conform to assumptions of machine learning algorithm. 2. Developed advanced graphic visualization concepts to map and simplify analysis of heavily-numeric data and reports. 3. Researched, designed and implemented machine learning applications to solve business problems affecting Millions of users. 4. Leveraged artificial intelligence and machine learning algorithms for standalone products and enhanced existing product offerings. 5. Design and develop Model Ops using Open Source ML-Flow, openshift, GCP 6. Design and develop pipelines using spark, scala, Python, Nifi, presto on CDP platform 7. Prototyped machine learning applications and quickly determined application viability 8. Tuned systems to boost performance 9. Authored code fixes and enhancements for inclusion in future code releases and patches

1. Write ETL job and Develop application using spark scala Applications. 2. Aws Cloud Migration, setting up MSK, EMR, Databricks 3. Helping Data Scientist to productionize the algorithms. 4. Design airflow DAGs 5. Productionize the algorithms (Deep and wide, neural networks, CF, random forests) along with etl jobs. 6. Cross functional with offshore teams and communicate day today activities 7. Write data pipelines to bring the data from API(paymentiq, Zendesk and Google Analytics) and ingest into Data Lake. 8. Design data models and build cloud based data lake for Qliro. 9. Involved in CDC design and implement the data pipelines to make use CDC

1. Develop etl tasks along with models deploy them in Azure 2. Generate recommendations for customers 3. Productionize the models

1. Bring the data to Hadoop from Solace/JMS Source to Hadoop using flume. 2. Wrote Custom interceptors to convert to protobuf to avro . 3. Involved in wirting UDFs 4.perform analytics using Datameer 5. Data governance. 6. Developing application that comply GDPR guide lines. 6. Deploy using Gocd (pipelines). 7. Bridge between the data engineering team and data science team. 8. Did POC on Kubernetes to make sure we can run containerized applications. 9. Develop application governing GDPR guide lines

Project Description: The aim of the project to perform trend analytics on customers in the current market. 1. Convert the Pl/Sql functionality to Scala code. 2. Create the Hive tables and load them with data from oracle. 3. Involved in end to end testing of applications. 4. Fine tune the performance of the applications. 5. Review the scala code. 6. Did POC on Apache Kudu to fit as alternative to Hive. 7. Worked closely with cloudera team to gain knowledge on Apache kudu and implemented Apache kudu in Dev Environment.

Project Description: The aim this project is to perform media analytics, with mobile advancing rapidly media analytics plays vital role. we have content providers around the globe, and needs to analyze logs quickly and provide accurate statistics on internet traffic. Akamai being pioneer among content deliver operators we have to store data and perform analytics on top of the data we host in our servers. Responsibilities: . Downloading the logs to hdfs from netstorage and filtering them asper asname. . Process the logs and extract the data required to the business for Analysis. . Analyzing data and creating data for the clients from the live data available. . Downloading the data from hdfs to local server and to extract the data as per requirement and visualize it for business. . Provide insights of reports . Part of NEA (Network Experience Analytics) product development team, to develop a tool for network performance analysis. . Parquet file generations from gz files.

Responsibilities: 1. Involved in functional requirement review. 2. Processed BigData using a Hadoop cluster 3. Involved in the PIG script development for data processing. 4. Involved in development of MR jobs. 5. Extensively used HIVE for querying large datasets. Creating hive tables and working on them. 6. Writing Scoop jobs for moving data between Relational Databases and HDFS. 7. Involved in Migration of code from Dev to Production Environment. 8. Involved in unit testing, system integration testing for the Hadoop jobs and prepared test cases. 9. Working in 24/7 environment to fix the production issues and identify root causes. 10. Derive insights from the analysis for the purpose of making recommendations.