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Dibyo Chakraborty

Software Engineering Intern at Neeto

Go (Golang)Large Language Models (LLM)Retrieval-Augmented Generation (RAG)Backend EngineeringDistributed SystemsMachine Learning Foundations

About

I am Dibyo Chakraborty, a Software Engineering Intern at Neeto and a Computer Science student at Scaler School of Technology. My journey has taken me from AI engineering at HandaUncle to my current focus on backend systems, always driven by a desire to understand the 'why' behind the 'how.' I specialize in Go, LLM infrastructure, and building distributed systems that are robust enough for production. I am passionate about first principles engineering and moving beyond simple abstractions to build systems that actually hold up. I'm looking to connect with fellow builders and engineers to share insights on backend architecture and the mathematical foundations of machine learning.

Networking

What I can offer

  • Expertise in Go and backend systems
  • Knowledge of LLM infrastructure, RAG, and embeddings
  • Implementation of ML pipelines and recommender systems

Looking for

  • expanding my professional network
  • exploring mutual opportunities in their industry

Best fit for

Software engineersAI/ML researchersBackend developersEngineering mentors

Current Interests

First Principles EngineeringSystem ReliabilityMathematics for MLVector SearchConcurrency

Background

Career

Transitioned from an AI Engineer Intern at HandaUncle to a Software Engineering Intern at Neeto while pursuing an undergraduate degree.

Education

Undergraduate student in Computer Science at Scaler School of Technology (Class of 2028).

Achievements

  • Secured internships in both AI Engineering and Software Engineering as an undergraduate.
  • Self-implemented recommender systems and rate-limiting algorithms from first principles.

Opinions

  • Building things from scratch is essential to ensure a deep understanding of core ideas.
  • Prioritizing systems that actually hold up in production over superficial implementation.
  • A strong preference for first principles thinking over relying on abstractions.