Anees Amjad

I build AI tools and data products for real-world decisions.

I’m a master’s student in Computational Analysis and Public Policy at the University of Chicago. I build AI workflows, data pipelines, and software across energy, public service, and research.

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Open to AI engineering roles, startup opportunities, and collaboration.

Question 1

How do students and early-career candidates find government roles in one place?

What I built

PublicPath — making public-sector opportunities easier to find.

I co-built PublicPath, a platform helping students and early-career candidates explore government careers. My work focused on API ingestion and data quality: cleaning, deduplicating, normalizing, and validating listings for search and filtering.

Role
Co-founder, Engineering and Data; one of two engineers
Tools
Python, GitHub Actions, Supabase, Postgres
Live
Project website (joinpublicpath.com) ↗ Job search was not loading when checked on 6 Oct 2026.

How it works

  1. Pull listings from external job APIs on a schedule
  2. Clean, deduplicate, normalize, validate
  3. Store in Postgres on Supabase
  4. Serve search and filtering on the site

Simplified sequence.

PublicPath homepage with the headline ‘Your path into public service starts here’.
Project homepage (team product), captured 6 October 2026.

Question 2

How can a team find what the company already knows?

What I built

Ourea Brain — company knowledge, accessible through Slack.

I built and deployed Ourea Brain, an internal AI assistant that brings company context into Slack. It connects information across Slack, Gmail, Google Docs, and meeting notes to help colleagues retrieve project context, identify owners, and follow up on decisions. The application runs on Google Cloud Run and uses Anthropic’s Claude API; an early live deployment answered a Slack question using meeting-note context.

Role
Builder · Agentic AI Intern, Ourea Energy
Status
Built and deployed internally

How it works

  1. A colleague asks a question in Slack
  2. The app on Google Cloud Run gathers relevant company context
  3. The Claude API drafts an answer from that context
  4. The response comes back in Slack

Simplified architecture.

Question 3

How can a clinical note become an explanation a patient can follow?

What I built

CLARITY — turning clinical notes into patient-friendly explanations.

I built a Python pipeline for the CLARITY healthcare-AI project that converts de-identified clinical-note JSON into patient-friendly explanatory videos. The workflow automates narration, slide generation, and MP4 production using Microsoft Neural TTS.

Context
Research Assistant, HOPE Lab, UChicago Booth
Tools
Python, Microsoft Neural TTS

How it works

  1. De-identified clinical note (JSON)
  2. Explanatory content
  3. Narration and slides
  4. MP4 video

Conceptual. Clinical material is not public, so no examples are shown.

More questions I’ve worked on

Background

I combine software building with applied research: data platforms and AI workflows, alongside machine learning, survey analysis, and geospatial work.