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ML Engineer with a chronic curiosity for weird and low-level tech
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Machine Learning Engineer @contentstack
Data Scientist
June 14, 2026 – Present
proxima
February 28, 2026 – Present
A C++17 implementation of HNSW Index for approximate nearest neighbour search, with SIMD acceleration and tunable indexing / search behaviour
View Projectneurogc
January 30, 2026 – Present
NeuroGC is an experimental system that models application behavior using deep learning to proactively schedule Python garbage collection, reducing latency spikes and memory pressure.
View Projectcython-exploration
January 16, 2026 – Present
A lightweight experimental matrix library built with Cython to explore how C/Python interops, build systems, and performance tuning
View Projectllm-rag-for-ui-creation
February 6, 2024 – April 1, 2024
Using LLMs to generate custom UI elements. Currently using Mistral-7b.
View Projectdsa-and-stuff
August 23, 2022 – Present
Collection of DSA and CP problems that I have solved primarily using Python, C++ and Go . Also contains some handy scripts that I use to ease my workflow.
View Projectml-from-scratch
May 26, 2022 – Present
A simple guide that contains manual implementations of machine learning and computer vision algorithms. It includes numpy, pandas, sklearn, opencv and pytorch.
View Projectcreative-sketches
April 1, 2022 – March 24, 2024
An everyday challenge to make something creative with code. Currently learning from Nature of Code by Daniel Shiffman.
View ProjectBankist-Website
January 29, 2021 – January 29, 2021
A website for the Bankist app. This was a one the segments in the Advanced JS Bootcamp and it focused in UI-related JS attributes which enables a smoother experience for the user. Check out the website here ⬇
View ProjectBankist-App
January 29, 2021 – January 29, 2021
A simple banking app with a touch of minimalism. This was a part of the Advanced JS Bootcamp. The topics which I learnt while making this include modern ES6 syntax, array and advanced array methods. You can check out this website here ⬇
View ProjectCultural Fit Analysis
The candidate demonstrates a strong inclination towards personal development and exploration, evidenced by numerous personal projects covering diverse technical areas from web development to advanced machine learning and low-level performance optimization. This breadth of interest and self-directed learning aligns well with a culture that values innovation and continuous improvement. The target role of 'Data Scientist' is well-supported by projects like 'llm-rag-for-ui-creation', 'ml-from-scratch', and 'neural-quines'. The 'dsa-and-stuff' project also indicates a solid foundation in computer science fundamentals. The current role as 'Machine Learning Engineer' further strengthens the alignment with data science and ML-focused environments.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven individual, eager to learn and experiment with new technologies. The diversity of personal projects suggests adaptability and a strong interest in continuous learning. However, without specific psychometric test results or interview data, it is difficult to assess communication clarity, teamwork, or stress handling directly. The 'experienceLevel' of 0, despite having a current role as 'Machine Learning Engineer', suggests a potential discrepancy or very early career stage, which might impact operational fit for senior roles.