Description
- Languages Python, C, HTML, CSS
- Database MySQL, Athena, MongoDB
- Frameworks Keras, Pandas, NumPy, TensorFlow, AWS Sagemaker, AWS lambda, Docker, Apache Spar
I worked as software engineer at Intel’s India office. I was involved in multiple projects such as duplicate bug finding application, predicting category of bugs with ML, Automation for Intel’s AI features for Evo branded laptop, Containerising AI models for profiling, integrating AI models with docker on multiple os on Intel NUC, Zephyr IoT RTOS open-source project, Android kernel optimization project. I was completely involved from proposal to designing software for duplicate bug finding application as well as prediction category of bugs. For this project I had worked with Python, keras, spacy, MongoDB, Reactjs, Flask, NodeJs, tensorflow, scikit-learn and related technologies and frameworks. To containarise AI models, I have applied transfer learning to publicly available. For Zephyr RTOS, I have written multiple device drivers to enable sensors (RGB, Ambient Light) on various Intel boards and up streamed to open-source repo. Apart from this, I ported legacy code for newly implemented APIs, implemented POSIX APIs, wrote python scripts to test atomic test cases and parse output to generate pass/fail report. All code for zephyr was in C language.
I am involved as machine learning engineer in omni markdown project which is responsible to predict traffic for future seasonal events. I am responsible to write code for various activity such as refactoring code from jupyter notebooks, alerts with aws lambda functions, data quality checks, Jenkins files for various github repo for CI/CD, converting SQL code from data engineers to pandas queries, writing/improving docker files. I am mainly working with Python, pandas, aws sagemaker, aws lambda, athena and redshift DB, keras, tensorflow, SQL, airflow and other related technologies and frameworks. Working in a team of 10 engineers with agile project development. Involved closely with other team members doing code reviews to ensure scalable, reusable and maintainable code.
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