Aman Malhotra
Sr. Software Development Engineer, AI Platforms
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolAman Malhotra is a Sr. Software Development Engineer with 10 years of experience building AI platforms and cloud-native systems at Amazon and AWS, focused on enabling efficient compute allocation and explainability for ML workloads. An IIT Roorkee graduate in Electrical Engineering with a Computer Science minor, he blends deep technical rigor with pragmatic delivery—learning software design "one sprint at a time." He is proficient in Python, AWS, Java, and NodeJS and has contributed open-source work to the widely used amazon-sagemaker-examples, adding online explainability features for NLP models. At AWS he moved from payments and HR systems into AI infrastructure, giving him a rare combination of customer-facing product experience and platform engineering depth. Based in the San Francisco Bay Area, he brings a hands-on engineering mindset to complex system integration and model interpretability challenges. Colleagues describe him as an engineer by passion who pairs thoughtful design with continuous, incremental improvement.
10 years of coding experience
7 years of employment as a software developer
Indian Institute of Technology RoorkeeSt. Paul's Church College, Agra
English, Hindi
Github Skills (8)
amazon-sagemaker10
jupyter-notebook10
machine-learning10
aws10
python10
nlp9
inference9
deep-learning8
Programming languages (2)
Github contributions (5)
aws/amazon-sagemaker-examples
Oct 2022 - Jan 2023
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:29 reviews, 4 commits, 6 PRs in 3 months
Contributions summary:Aman's contributions primarily revolve around developing and integrating online explainability features within the Amazon SageMaker ecosystem, specifically for NLP models. Their work includes implementing new notebooks for the SageMaker Clarify integration, focusing on feature additions, and addressing code improvements. The user updated the install commands for required libraries and made other code-level adjustments to integrate and test these new features. These changes enhance model explainability and demonstrate a focus on machine learning model deployment and interpretation.
pythonjupyter-notebooktrainingawssagemaker
malhotra18/amazon-sagemaker-examples
Sep 2022 - Jun 2023
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Contributions:38 pushes, 10 branches in 8 months
sagemakerpythonamazon-sagemakermachine-learning-deploydata-science
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