Hrushikesh Dokala
Software Engineer II at VEnable AI
Hyderabad, Telangana, India
Summary
🤩
Rockstar🎓
Top SchoolHrushikesh Dokala is a Software Engineer II based in Hyderabad who sits at the intersection of systems engineering and AI, with three years of professional experience building agentic workflows and metadata platforms. At Atlan he architected the Model Context Protocol and server that let LLM-based agents query and manipulate metadata, and contributed to production-grade RAG pipelines and intent parsing. He’s the creator of snaildb and an active open-source contributor—having improved cost-tracking and usage summaries in Microsoft’s popular AutoGen framework—demonstrating attention to both developer UX and backend reliability. As a founding member at VEnable AI and former startup COO, he blends hands-on engineering with product and go-to-market instincts. Strong academic performance and community leadership (Microsoft Student Community Hyderabad) round out a profile that favors building practical AI systems that integrate deeply with data infrastructure.
3 years of coding experience
3 years of employment as a software developer
Bachelor of Technology - BTech, Computer Science, Bachelor of Technology - BTech, Computer Science at INSTITUTE OF AERONAUTICAL ENGINEERINGIntermediate, 96.6%, Intermediate, 96.6% at Sri Chaitanya College of EducationHigh School, 88%, High School, 88% at kennedy high the magnet school
Github Skills (6)
autogen10
agentic-ai10
python10
testing9
api8
apidoc8
Programming languages (6)
Github contributions (5)
microsoft/autogen
Mar 2024 - Oct 2024
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
Role in this project:
Back-end Developer
Back-end DeveloperContributions:184 reviews, 25 PRs, 94 pushes in 6 months
Contributions summary:Hrushikesh made significant contributions to improving the cost tracking and usage summary functionalities within the AutoGen framework. They updated the `gather_usage_summary` function to provide more readable cost information, including cached and uncached inference costs. The user refactored the `agentchat/utils.py` and `test/agentchat/test_agent_usage.py` files to reflect these changes, and added related fixes and documentation updates. Further contributions include fixes to file logger and Anthropic Client.
agenticagentic-agiagentsaiautogen
Hk669/Open-Source-Recommender
Jul 2023 - Aug 2024
personalized recommendations of open-source repositories based on your skills and languages from your GitHub
Contributions:14 PRs, 102 pushes, 17 branches in 1 year 1 month
aiawsazure-openaichromadbgood-first-issue
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