I am a Software Engineer exploring the core of LLMs, Generative AI, and Multi-Agent Systems, with a strong foundation in Data Analytics, Machine Learning, AWS, and DevOps, alongside experience in Web and Android Development. Proficient in Python, Java, JavaScript, SQL, SAS, and databases like MongoDB, Neo4j, Qdrant, I specialize in building scalable, efficient solutions that combine cloud-native design with state-of-the-art AI tools.
My expertise extends across Pytorch, TensorFlow, Langflow, Langchain, Langgraph, Autogen, and MCP, enabling me to design advanced RAG pipelines, AI agents, and multi-cloud architectures. With AWS services (Athena, S3, Lambda, QuickSight, Glue, Bedrock, Neptune, SageMaker), I optimize data workflows and deploy AI-driven applications that deliver measurable impact.
- Develop and optimize RAG pipelines (e.g., CFM-RAG with multimodal retrieval, improving accuracy by 35%).
- Engineer multi-agent systems (e.g., Planner and Orchestrator Agents at Volkswagen, reducing misrouted tickets by 30%).
- Design cloud-native solutions with AWS, cutting deployment costs and boosting throughput by up to 40%.
- Design and deploy robust Machine Learning models that extract actionable insights from complex data to drive intelligent decision-making.
- π₯ 1st Place (National Winner) β i.Mobilothon 4.0 (EV battery optimization with VWITS).
- π Top 10 Finalist β Flipkart Grid 6.0 (Crystal Quantum Shield for API Security).
- π― Finalist β Woodpeckerβs Hackathon (real-time disaster prediction system).
- π 500+ coding problems solved with a 61.27% acceptance rate on LeetCode & HackerRank.
- π Certified in SAS Programming (Internshala, Score: 84%).
- π Working on: Knowledge Graphs, Advanced RAG, Langflow Custom Components, Langchain/Agentic Workflows.
- π± Learning: Bedrock Integration in Langflow, Multi-Agent Orchestration, Custom AI Components.
- π¬ Ask me about: AI Agents, RAG Optimization, Reingforcement Learning, Knowledge graph, Langflow, Langchain, Langgraph, Docker, AWS, Competitive Programming.
- π€ Open for: Contributions in Open Source Projects.
- π¨βπ» My Portfolio: Portfolio.
- β‘ Fun Fact: I believe debugging is 50% logic, 50% detective work.
- QWhale & SARSAWhale Hybrid Reinforcement Learning Algorithm for Energy Efficient Optimization and Scheduling
- QWhale and SARSAWhale: Energy-Efficient and Energy-Aware Algorithms for High-Load Cloud Environments
- Crystal Quantum Shield (CQS): A Post-Quantum Cybersecurity Framework for API and Data Protection
Copyright (c) 2022-2032 Ayush Verma and the PerfectCube Team
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