Help & Supportcareers@gravityer.com
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Software Engineer II @ Amazon | ML Infrastructure, Distributed Systems & Large-Scale Inference
I am a Software Engineer specializing in large-scale ML inference and distributed systems. Currently, I work on the infrastructure powering Amazon Rufus, optimizing LLM serving pipelines across AWS to handle massive throughput. My core expertise lies in architecting efficient AI platforms—from tuning low-level kernel performance on custom silicon (Inferentia/Trainium) to designing automated release pipelines that ensure reliability at scale. When I'm not optimizing distributed systems, you can find me managing my own rack-mounted homelab, rock climbing, or woodworking. Core Tech: Python, PyTorch, vLLM, AWS (ECS, Lambda, EFA), Distributed Inference, CI/CD.
University of Southern California
Master's degree, Computer Science
January 1, 2019 – January 1, 2021
SGTB Khalsa College, University of Delhi
Bachelor of Technology (B.Tech.), Computer Science
January 1, 2013 – January 1, 2017
Tagore International School
Science
January 1, 2011 – January 1, 2013
Amazon
Software Development Engineer
February 1, 2021 – Present
Seattle, Washington, United States
Amazon
Software Development Engineer Intern
May 1, 2020 – August 1, 2020
Seattle, Washington, United States
Robotics Embedded Systems Lab, Viterbi School of Engineering, USC
Research Volunteer
November 1, 2019 – December 1, 2020
Los Angeles Metropolitan Area
USC Rossier School of Education
Student Worker
August 1, 2019 – December 1, 2020
Los Angeles Metropolitan Area
Veative Labs
Artificial Intelligence Specialist
June 1, 2017 – August 1, 2018
Noida, Uttar Pradesh, India
str8bat Sport Tech Solutions Pvt. Ltd.
Intern
December 1, 2016 – April 1, 2017
Bangalore
Defence Research and Development Organisation (DRDO)
Summer Intern
June 1, 2015 – July 1, 2015
Greater Delhi Area
INTEGER Innovation
Summer Training
July 1, 2014 – August 1, 2014
Aedifico Tech Pvt. Ltd.
Summer Training
June 1, 2014 – August 1, 2014
Noida, Uttar Pradesh, India
Enterprise Homelab & Private Cloud
January 1, 2025 – Present
Architected a private server environment using Proxmox and ZFS to self-host local LLMs and storage. Configured OPNsense for network segmentation (VLANs) and automated LXC container deployments for network security and automation.
Study of Ordering In Data
August 1, 2016 – Present
Final Year Project - To identify inherent partial orders in data and then develop and apply several statistical hypothesis to draw inferences from the data.
Network Playable Battleship Game
January 1, 2016 – April 1, 2016
Semester Project, Network Programming - Implemented a version of the popular game Battleship which can be played over a Network using Python and TKinter
Robot Control Language
January 1, 2016 – April 1, 2016
Semester Project, Compiler Design - Designed and implemented a robot control language using language parsers (yacc, bison)
Predicting Product Lifecyles using Weibull Distribution
September 1, 2015 – November 1, 2015
Semester Project, Probability Theory and Statistical Computing - Implemented Weibull Distribution to predict the life cycle of a product using experimental data
Markov Chains for Path Prediction
February 1, 2014 – April 1, 2014
Semester Project, Linear Algebra for Computer Science - Utilised an algorithm which uses Markov Chains to predict path for a robotic agent
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from enterprise homelabs to academic research and large-scale industry projects, indicates a strong curiosity and a drive for continuous learning. Their experience in both startup environments (Veative Labs, str8bat) and large corporations (Amazon) suggests adaptability to different organizational cultures. The focus on ML engineering and distributed systems aligns well with a high-growth, technically challenging environment.
Soft Skills & Operational Fit
The candidate's experience at Amazon and in research roles suggests strong problem-solving, collaboration, and adaptability. The ability to lead projects like the automated release pipeline and contribute to critical infrastructure indicates a proactive and ownership-driven approach. The diverse project background also points to a strong learning aptitude and ability to work across different technical domains.