Pre-final year CSE student passionate about multi-agent AI systems. Currently a Data Analytics & AI Apprentice at PwC Acceleration Centers India. I build real products — not prototypes.
Last updated · April 2025 · Bengaluru, IN
Pre-final year B.Tech CSE at Manipal Institute of Technology, Bengaluru (CGPA 7.98). Currently a Data Analytics & AI Apprentice at PwC Acceleration Centers India.
My target is Agentic AI Engineering — autonomous systems that plan, execute, and recover. I go from idea to deployed product, fast.
Top 10 · HackNite'25 & RILO 2025
CECAS-MITB Placement Committee
McKinsey Forward Program · 2026
Build a real-time fire detection and early-warning system using open civic datasets. The challenge required integrating live government data sources to produce actionable alerts with minimal latency.
Built a fire detection and alarming tool using live San Francisco fire incident datasets via the Socrata Open Data API. The system polls real-time incident feeds, runs a classification layer to filter fire-type incidents, and triggers graded alerts based on proximity and severity. Includes a lightweight dashboard for monitoring active incidents.
· Participated · Hackbricks 2026
Design an AI system for autonomous vehicle perception — specifically drivable space detection — under a strict constraint: no pretrained model weights allowed. All models must be trained from scratch on the provided nuScenes dataset.
Built FreeSeg: a custom encoder-decoder segmentation model entirely from scratch in PyTorch. Used a CNN encoder with residual shortcuts and a U-Net-style decoder with skip connections. Compensated for no pretrained generalisation through aggressive augmentation — random crops, colour jitter, flips, Gaussian blur. Achieved ~0.68 mean IoU on drivable surface class.
· Submitted · AI in Mobility Track
Use the Databricks lakehouse stack to build a meaningful, production-grade data product. The challenge encouraged creative application of Databricks tools including MLflow, Delta Lake, and the Medallion architecture.
Built HabitEcho, a passive phone behaviour monitoring system. Ingested raw phone usage logs through a Bronze → Silver → Gold Medallion pipeline on Databricks. Applied Granger causality analysis to surface which app behaviours predict productivity outcomes, and used Isolation Forest for anomaly detection in usage patterns. Tracked all experiments with MLflow.
· Submitted · Databricks Hackathon 2026
Design an intelligent IT operations solution that reduces mean time to resolution (MTTR) for enterprise incidents. The solution needed to demonstrate real agentic capability — not just a chatbot wrapper around ticket lookup.
Built AutoResolve: a 4-agent LangGraph pipeline with Triage → Diagnostic → Remediation → Verification stages. All agents communicate via a typed shared state object. Groq powers fast inference. Pre-approved remediation playbooks are executed by the Remediation Agent. Human-in-the-loop gates enforce review before any severity-1 action executes.
✓ Submitted · Unisys UIP Y17
Build a product that solves a real pain point for students or early-career professionals, leveraging AI. The hackathon was organised by Zomato and emphasized product thinking alongside technical execution.
PrepSense was an AI-powered interview preparation platform. It generated personalised interview question sets based on job description + resume, tracked performance across mock sessions, and surfaced improvement patterns. Used NLP to identify skill gaps between the target JD and the user's current responses, then recommended targeted preparation paths.
· Submitted · Zomathon 2026
Actively looking for internship and early-career roles in Agentic AI Engineering. Open to full-time after graduation in 2027.