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Avik Bhadra

@avikbhadra

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I'm building agentic analytics and LLM-powered products for real-world business decisions.

India

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What I'm looking for

I'm looking to build scalable AI and analytics products where I can own end-to-end development, from prototyping and ML research to deployment, while solving practical business problems.

I'm building Nexus Analytics, an agentic B2B analytics platform for Indian SMEs in logistics and fintech. As co-founder and lead engineer, I architected a seven-agent Cognitive Mesh, persistent cross-session memory, forecasting ensembles, and real-time anomaly detection pipelines.

At Immersive Trails, I build AI orchestration for personalized travel itineraries with stateful multi-turn reasoning. I'm also researching autoencoder-based financial crisis forecasting at Heritage Institute of Technology, applying deep learning to Indian macroeconomic time-series data.

Experience

Work history, roles, and key accomplishments

Current

AI Implementation Intern

Current

Immersive Trails

Jun 2026 - Present (3 months)

Built an AI orchestration layer to dynamically generate personalized travel itineraries from real-time user profiles. Implemented memory management and context-routing for coherent multi-turn itinerary generation.

AI Orchestration Memory Management Context Routing Personalization Travel Itinerary Planning

Current

Research Internship

Current

Heritage Institute of Technology

Jan 2025 - Present (1 year 8 months)

Contributing to a research paper on financial crisis forecasting using an autoencoder-based deep learning model. Designing a Temporal Convolutional Autoencoder for anomaly detection in Indian macroeconomic time-series data.

Deep Learning Autoencoder Time Series Analysis Anomaly Detection Research Python PyTorch

Education

Degrees, certifications, and relevant coursework

Heritage Institute of Technology

B.Tech, Computer Science

2024 -

Grade: 8.8 CGPA

Activities and societies: Contributed to various on-campus AI research and innovation projects.

Pursuing a B.Tech in Computer Science with a CGPA of 8.8, expected to graduate in 2028. Coursework includes Data Structures & Algorithms, Machine Learning, and Database Management Systems.