Subject: Ansh Sharma

Code: P-33

Log Date: --.--.----

Observer: Self

fingerprint Professional Persona

Subject demonstrates an unusual tendency to over-think solutions to problems. Specializes in data engineering and ML systems with a particular interest in how infrastructure decisions affect the people actually using the software — which sometimes leads to questionable rabbit holes. Has a knack for this, which explains the architectural depth and lack of sleep schedule. Targeting roles in data engineering, ML, and AI/LLM systems.

Current Status

Experimental Overview (Projects)

EXP_01

Spore

Flagship

*Now with more than 150+ docker pulls.

Local-first data platform for environments where "just use the cloud" is not an option. Subject built this unprompted. Threat model: realistic. Ambition level: concerning.

Spore Diagram
Validation: Processed 6.7GB TSV dataset successfully. Peak memory usage contained within target thresholds.
EXP_02

Wingman

Legacy

Local LLM Integration Environment.

VS Code extension for AI-assisted development without sending your codebase to a third party. Runs entirely on local LLM instances.

VS Code Extension Ollama Local AI
DEPLOYED

DAM System

To Be Replaced

Production-ready Digital Asset Management architecture.

Status

Production-deployed asset management system. OAuth-secured. Maybe still running. Considered legacy by subject — included for completeness and proof of shipping.

folder_managed
→
PROD

menu_book Academia

Data Annotation and Collection Project

Contributed in a academic project on code-mixed Hinglish text classification. Work involved multi-platform data collection across Reddit, Twitter, Instagram, Facebook, YouTube, and Telegram — each with its own access constraints and workarounds.

Also handled corpus annotation and proposed a confidence-threshold based annotation pipeline to reduce manual bottleneck on a ~25k record dataset.

build Technical Core

  • Languages: Python, R, SQL, NoSQL
  • Databases: PostgreSQL, MySQL, MongoDB, Supabase
  • Data: Pandas, NumPy, Apache Arrow, Apache Parquet, DuckDB
  • AI / ML: Scikit-learn, NLTK
  • LLM Tooling: LangChain, Hugging Face Transformers, Ollama, RAG, Vector Embeddings
  • Infrastructure: Docker, Redis, Apache Superset, Flask, Git