Mayank Kaushik
Associate Vice President - Business Development
New Jersey, United States
Summary
🤩
Rockstar🎓
Top SchoolMayank Kaushik is an Associate Vice President focused on turning AI and data into measurable competitive advantage, blending business development leadership with hands-on technical contributions. With over a decade of experience in digital transformation, product strategy, and client-facing roles, he leads cross-functional teams to prototype, scope, and deliver customer-centric solutions that drive ROI. Based in New Jersey, he combines an MBA in IT with a BBA foundation to move between strategy, design, and execution fluently. Though primarily a business leader, he contributes to open-source AI infrastructure—working on the back end of an AI agent memory layer that improved graph memory support, embedding handling, and data retrieval optimizations. Mayank is known for cultivating trusted client relationships while quietly shipping technical improvements that bridge product strategy and scalable engineering. His profile reflects a rare mix of designer sensibility, commercial acumen, and substantive developer contributions.
5 years of coding experience
13 years of employment as a software developer
Master of Business Administration - MBA, Information Technology, Master of Business Administration - MBA, Information Technology at Gauhati UniversityUniversity of Delhi
English, Hindi, Punjabi
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (22)
python10
redis10
memory-management10
vector-database10
graph-database10
llm10
agent10
ai10
embedding9
api8
azure-openai8
api-design8
wpgraphql7
wordpress-graphql7
huggingface7
Programming languages (8)
Github contributions (5)
mem0ai/mem0
Sep 2024 - Feb 2025
The Memory layer for AI Agents
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 reviews, 27 PRs, 12 pushes in 5 months
Contributions summary:Mayank primarily contributed to the back-end logic of the AI agent's memory layer. Their work included implementing graph memory support for non-structured models, removing duplicate embedding generation, and fixing bugs related to memory reset functionality. They also integrated a new embedding provider (Together AI) and improved the extraction of graph nodes. The user's work focused heavily on data storage and retrieval optimizations.
agentaiaiagentapplicationchatbots
spike-spiegel-21/langchain
Mar 2024 - Jul 2024
🦜🔗 Build context-aware reasoning applications
Contributions:36 pushes, 8 branches in 3 months
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