Pradeep Nalabalapu
Verified Expert in Engineering
Software Developer
Pradeep is a software engineer with experience in data engineering and machine learning. He has recently worked on machine learning and ETL using Python PySpark on platforms like Cloudera, Databricks, Azure, and AWS. Pradeep also has experience programming using C, C++, Java, Scala, and JavaScript and has several years of semiconductor industry experience.
Portfolio
Experience
Availability
Preferred Environment
C++, C, Python, MacOS, Linux
The most amazing...
...thing I've done was to train a neural network to detect a trigger word in audio samples.
Work Experience
Lead Data Scientist
DeepIQ
- Developed the back end of the in-house ETL software, using PySpark.
- Constructed ML models for predicting oil resource density based on location.
- Created an ETL solution that runs on AWS Glue for a retail client to process transaction data; also imported custom PySpark scripts into Glue.
Senior Machine Learning Engineer
MapR Technologies
- Worked on an ETL solution for a retail client (Chico's).
- Developed with Java and used MapR Streams and MapR-DB.
- Built a machine learning deployment demo; worked on both the React front end and Scala/Spark web-server back end.
- Constructed a generic item-similarity based recommendation engine; used the Spark engine with Scala.
- Created a proof of concept (POC) for scaling a distributed app using Docker and Kubernetes.
Software Developer
Self-employed
- Developed C++ software to capture data from a bedside patient monitor (Philips MP70).
Consultant
Clarity Insights
- Primarily worked on ETL for a new data warehousing solution being developed on AWS.
- Developed PySpark programs to clean up and extract columns from existing in-house data sources.
- Assisted the data architect in refining the schema and defining transformations from existing data columns.
Data Scientist
Clemetric
- Extract aggregates and statistics on huge amounts of insurance claims data (Python and Apache Spark).
- Developed machine learning models for health insurance claims data (Python Scikit-learn, NumPy, and Pandas).
- Performed ETL on health insurance claims data, using Python and PostgreSQL.
- Developed SQL stored procedures.
- Built-up the back end for a web app that served a data stream.
- Developed an API using Node.js and Python.
- Created a simple front end and charts to display patient vitals (AngularJS, Highcharts, and D3.js).
- Developed C/C++ implementations of medical data analysis algorithms (MATLAB).
Staff Verification Engineer
Qualcomm, Inc.
- Worked primarily as a hardware verification engineer on the development of test benches and test suite for verifying hardware blocks in a video decoder.
- Built an interface between the C++ based system model and OVM-based test bench.
Member of Technical Staff
Ambarella Corporation
- Worked on various stages of hardware verification at Ambarella.
- Verified the memory subsystem.
- Developed the DRAM controller programming portion of BIOS and DRAM controller bring-up in the lab.
- Built some C++ system models that were part of the software model of the chip.
Experience
My GitHub Page
https://github.com/pnalabaEducation
Master of Science (MSc) Degree in Computer Engineering
Clemson University - Clemson, SC, USA
Bachelor of Technology Degree in Electrical Engineering
Indian Institute of Technology Madras - Chennai, India
Certifications
End-to-end Machine Learning with TensorFlow on GCP
Coursera
Production Machine Learning Systems
Coursera
Machine Learning with TensorFlow on the Google Cloud Platform Specialization
Coursera
Sequence Models
Coursera
Convolutional Neural Networks
Coursera
Deep Learning Specialization
Coursera
Skills
Languages
Python, C++, Java, Verilog, SystemVerilog, C, Java 8, JavaScript
Libraries/APIs
PySpark, Spark ML, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, Node.js, React
Other
Data Engineering, Machine Learning, Software Development, Back-end Development, Web Development
Frameworks
AngularJS, ASM, Spark
Tools
LaTeX, Cloudera, Amazon Elastic MapReduce (EMR)
Platforms
MacOS, Docker, Linux, MapR, Google Cloud Platform (GCP), Amazon Web Services (AWS)
Storage
Redshift, PostgreSQL, Amazon S3 (AWS S3), MapR-DB
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