Ruturaj Shitole
Software Engineer at Spectro Cloud
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolRuturaj Shitole is a software engineer with nine years of experience focused on cloud-native and ML infrastructure, now working at Spectro Cloud in Virginia. He has a strong track record at Syncron building vendor-agnostic AI infrastructure, multi-tenant feature stores, and event-driven CI/CD that accelerated ML delivery and cut operational costs significantly. Ruturaj combines hands-on engineering with practical product impact—delivering a revenue-driving price optimization app within two weeks and building services that scale to millions of features and hundreds of tenants. While completing an MS in Computer Science at George Washington University he also supported systems and computer-architecture courses by maintaining autograding and lab infrastructure, reflecting an interest in developer tooling and reproducible workflows. Colleagues describe him as an "on-prem Chad"—a hint at his pragmatic, get-things-done approach to complex on-prem and hybrid deployments.
9 years of coding experience
3 years of employment as a software developer
Bengaluru UniversityMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at The George Washington UniversityKendriya Vidyalaya Sangathan
English, Hindi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (4)
robotics7
representation-learning6
science6
data-processing2
Programming languages (5)
Github contributions (5)
ceevaaa/Eyantra-2016
Jan 2020 - Jun 2022
e-Yantra Robotics Competition (eYRC) is a unique annual competition for undergraduate students in science and engineering colleges
Contributions:4 commits, 1 PR, 2 pushes in 2 years 5 months
undergraduateroboticssciencerobotics-competitionengineering
A GNN based approach to model player compatibility in Multiplayer Online Battle Arena (MOBA) games like Dota2.
Contributions:22 PRs, 10 pushes, 11 branches in 1 year 1 month
deep-graph-infomaxdota2graph-neural-networksheterogeneous-graph-neural-networkrepresentation-learning
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