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Web Search for AI agents @ Parallel | Ex-AWS LLM Systems
Hello, Rahul here! I work in the intersection of AI and computer systems, helping the AI overlords establish their presence in the world. I am currently working on building web search for AI @ Parallel Web Systems. Previously I worked on building the distributed training software stack Neuron for Amazon’s deep learning chips and before that on distributed Model Parallelism for AWS SageMaker to train large models at scale on GPUs. I’ve also worked on SageMaker Debugger, TensorFlow, and MXNet all at AWS. Feel free to check out detailed descriptions and references of my work listed below.
The University of Texas at Austin
Master of Science (M.S.), Computer Science
January 1, 2015 – June 1, 2017
Indian Institute of Technology, Guwahati
Bachelor of Technology, Computer Science and Engineering
January 1, 2011 – January 1, 2015
Sri Chaitanya Junior College
Senior Secondary Education, Math, Physics, Chemistry
January 1, 2009 – January 1, 2011
Sanghamitra School
Secondary Education
January 1, 2002 – January 1, 2009
Parallel Web Systems
Member of Technical Staff
February 1, 2026 – Present
Palo Alto, California, United States · On-site
Amazon Web Services (AWS)
Senior Software Engineer
June 1, 2024 – January 1, 2026
Amazon Web Services (AWS)
Senior Software Engineer
October 1, 2022 – May 1, 2024
Amazon Web Services (AWS)
Software Engineer II
October 1, 2018 – September 1, 2022
Amazon Web Services (AWS)
Software Engineer
July 1, 2017 – September 1, 2018
The University of Texas at Austin
Graduate Teaching Assistant
September 1, 2016 – May 1, 2017
Austin, Texas Metropolitan Area
Bloomreach
Engineering Intern
May 1, 2016 – August 1, 2016
Mountain View, CA
The University of Texas at Austin
Graduate Research Assistant
February 1, 2016 – May 1, 2016
Austin, Texas Metropolitan Area
Université de Montréal
Research Intern
May 1, 2014 – July 1, 2014
Montreal, Canada
Machine Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for a challenging, innovation-driven environment, evidenced by their continuous progression and leadership roles at AWS, a company known for its demanding technical culture. Their involvement in open-source (Apache Committer for MXNet) and research internships (Université de Montréal with Yoshua Bengio) indicates a proactive, learning-oriented mindset. The breadth of experience from distributed systems to deep learning optimization shows adaptability and a willingness to tackle diverse technical challenges. The target role of ML Engineer aligns perfectly with their career trajectory and specialized skills.
Soft Skills & Operational Fit
The candidate's experience descriptions indicate strong leadership and collaboration skills, particularly in leading teams and working with compiler and collectives teams. Their work on designing and developing new systems (e.g., SageMaker Debugger, distributed feed management) suggests strong problem-solving and architectural thinking. The descriptions are detailed, implying good communication of complex technical concepts.