Harsh Verma@vharsh2615709
3
Events attended
1
Submissions made
India
1 year of experience
About me
AI/ML Engineer & Data Science Practitioner | Hackathon Project Lead I am an AI/ML developer and hackathon project leader passionate about architecting scalable solutions using Generative AI, machine learning, and cloud technologies. Currently advancing my expertise through a Master’s in Data Science at Deakin University and having completed a PGP in Artificial Intelligence & Machine Learning from UT Austin, I bridge the gap between deep academic theory and rapid, hands-on execution. As the project lead for the Sūtradhāra team in the Gen AI Academy APAC, I specialize in driving cross-functional initiatives from conceptualization to deployment. My applied experience as an independent contractor and developer spans workflow automation, algorithmic trading model development, and building robust, submission-ready AI applications. I thrive in high-stakes environments, combining technical acumen with strategic leadership to deliver tools that perfectly balance cutting-edge innovation with data privacy and compliance. Core Competencies & Keywords: Generative AI (LLMs, RAG, Agentic AI), Machine Learning, Data Science, Cloud Computing, Algorithmic Trading, Workflow Optimization, Cross-Functional Leadership, Rapid Prototyping, Technical Communication, Product Strategy.
🤝 Top Collaborators
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🤓 Latest Submissions
Nadi AI: Autonomous Multi-Agent Response Pipeline
Our project introduces a fully automated, multi-agent incident response system designed to reduce software downtime from hours to seconds. By leveraging the Band SDK for seamless agent-to-agent communication, the system operates as a continuous pipeline that detects, diagnoses, and resolves runtime errors without manual intervention. The Architecture: Agent 1: The Sentry (Diagnostics & Triaging): Acting as the first line of defense, this agent intercepts raw, unstructured crash logs. Using LangChain and Qwen2.5-Coder, it deterministically parses messy logs into strict, Pydantic-validated JSON payloads (extracting error type, severity, and stack traces) and securely publishes them to a designated Band Room. Agent 2: The Patch Engineer (Resolution): Operating asynchronously, this agent monitors the Band Room for new Sentry reports. Upon receiving a structured error payload, it utilizes Mistral (via the Featherless AI API) to analyze the fault and generate a targeted, executable code patch. Agent 3: Command Center & Reviewer (Oversight): The entire ecosystem is visualized through a centralized Streamlit dashboard. Before any code is pushed, a CrewAI-powered Reviewer Agent evaluates the Patch Engineer's proposed fix for security and structural integrity, ensuring human-in-the-loop visibility and control. Key Technologies: Band SDK, LangChain, Qwen2.5-Coder, Mistral (Featherless AI), CrewAI, Streamlit, and Pydantic. Impact: By combining structured LLM outputs with multi-agent networking, we have created a scalable, intelligent DevOps automation tool that treats runtime errors not as blockers, but as instantly solvable data events.
19 Jun 2026
