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SuperfiedStudd/README.md

Jasjyot Singh

AI systems builder focused on turning workflow friction into production-grade AI applications.
I build LLM-driven systems, analytics products, and decision tools across product, backend, and evaluation workflows.

What I Build

  • AI Systems & Eval Platforms: multi-model orchestration, prompt pipelines, judging workflows, human-in-the-loop review
  • Analytics & Decision Tools: dashboards, KPI systems, forecasting workflows, business intelligence apps
  • Productized Internal Tools: operational consoles, workflow automation, data products, scoring systems
  • End-to-End Prototypes: fast MVPs using modern full-stack tools with real backend logic

Tech Stack

Languages: Python, SQL, JavaScript, R
AI / Data: PyTorch, pandas, scikit-learn, LLM orchestration, evaluation systems, experimentation, RAG, feature engineering, feature stores
Big Data / ML Systems: PySpark, Apache Spark, Kafka, Flink, Databricks, Snowflake, BigQuery, Delta Lake, dbt, Feast, MLflow
Backend / Infra: Supabase, Postgres, REST APIs, Git, Docker, Kubernetes, Google Cloud
AI Dev Tools: Cursor, Claude Code, Codex, Google Antigravity, Lovable
Analytics / BI: Power BI, Tableau, ETL, data modeling
Other: Streamlit, Jupyter, RStudio

Featured Projects

Project What It Does
AI Evals Orchestration End-to-end evaluation workflow for comparing model outputs, saving decisions, and supporting human review
Trust Pulse Subscription abuse operations console linking accounts, payment methods, devices, IPs, and clusters for investigation
Recommendation Quality Lab Offline recommender experimentation lab with temporal splits, candidate generation, ranking, and beyond-accuracy metrics
Behavioral Prediction Engine Hackathon group project combining behavioral profiling, ML, and LLM-based prediction to forecast how real people would answer hidden survey questions
Context Parking Structured context management system for saving, retrieving, and reusing work context across AI workflows
Power BI Apps Showcase Showcase of 15 Power BI apps built for SMB reporting across sales, finance, inventory, operations, and production

Current Focus

  • Building AI products that look and feel production-ready
  • Combining product thinking with backend execution
  • Exploring eval systems, trust/risk workflows, and decision-support tools
  • Shipping projects that are recruiter-friendly and demoable

Say Hello

Looking For

Full-time opportunities in AI Product Management, Product, Data, and analytics-focused roles across the US, especially San Francisco and other major cities.

Pinned Loading

  1. recommendation-quality-lab recommendation-quality-lab Public

    Local-first recommendation strategy lab for simulating ranking tradeoffs, testing preset scenarios, and comparing recommendation performance through a Streamlit UI and CLI.

    Python 1

  2. context-parking context-parking Public

    Chrome extension and local-first workflow tool that saves AI chat context into structured cards, helping users organize, revisit, and resume work across sessions.

    TypeScript 1

  3. ai-evals-orchestration ai-evals-orchestration Public

    End-to-end AI evals orchestration platform for comparing LLM outputs across providers with transcription, structured logging, human review, and Supabase-backed decision tracking.

    TypeScript 1

  4. trust-pulse trust-pulse Public

    Subscription abuse operations console — event ingestion, heuristic risk scoring, trust graph visualization, and operator workflows. Built with React, Supabase, D3.

    TypeScript

  5. Power-BI-Apps-Showcase Power-BI-Apps-Showcase Public

    Showcase of 15 Power BI apps built to solve real SMB reporting problems across sales, finance, inventory, operations, and production, with live demos, AppSource listings, and supporting repos.

    1

  6. TanmayKallakuri/nexus TanmayKallakuri/nexus Public

    Hackathon Collaboration Hub — Team data analysis & reporting

    Python 1