01 / 06

Anees Amjad

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

My work spans an internal AI assistant at Ourea Energy, a public-sector careers platform I co-founded, and research software. I’m pursuing an MS in Computational Analysis and Public Policy at the University of Chicago.

Selected workGet in touch

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

  1. 02Ourea Brain
  2. 03PublicPath
  3. 04CLARITY
  4. 05Supporting work

02 / 06 · Built and deployed internally

Ourea Brain

Company knowledge, accessible through Slack.

What it does
Brings company context into Slack for colleagues at Ourea Energy, designed to help them retrieve project context, identify owners, and follow up on decisions.
What I built
As an Agentic AI Intern, I built and deployed the assistant: a Slack-connected app on Google Cloud Run drawing on Slack, Gmail, Google Docs, and meeting notes, using Anthropic’s Claude API.
Observed behaviour
An early live deployment answered a Slack question using meeting-note context.

Explanatory diagram

  1. Question asked in Slack
  2. App on Google Cloud Run
  3. Company context + Claude API
  4. Response in Slack
Simplified architecture; highlighted stages show my implementation.

03 / 06 · Co-founder, Engineering and Data

PublicPath

Making public-sector opportunities easier to find.

What it does
Helps students and early-career candidates explore government careers.
What I built
As one of two engineers, I worked on API ingestion (scheduled Python on GitHub Actions) and data quality: cleaning, deduplicating, normalizing, and validating listings stored in Postgres on Supabase.
Project website
joinpublicpath.com ↗Job search was not loading when checked on 6 Oct 2026.

Project artifact

PublicPath homepage with the headline ‘Your path into public service starts here’.
PublicPath team homepage, captured 6 October 2026. My contribution focused on ingestion and data quality.

04 / 06 · Research Assistant, HOPE Lab, UChicago Booth

CLARITY

Turning clinical notes into patient-friendly explanations.

What it does
Part of CLARITY, a healthcare-AI research project: turning de-identified clinical notes into patient-friendly explanatory videos.
What I built
The Python pipeline that automates explanatory content, narration with Microsoft Neural TTS, slide generation, and MP4 production.
Output
MP4 explanatory videos. Clinical material is not public, so no example is shown.

Explanatory diagram

  1. De-identified clinical note (JSON)
  2. Explanatory content
  3. Narration (Neural TTS) and slides
  4. MP4 video
Simplified sequence automated by my Python pipeline.

05 / 06

Supporting work

  • Energy Market Intelligence

    Ourea Energy · separate from Ourea Brain

    Built a weekly AI-assisted workflow that filtered 50+ recurring signals across energy storage, utilities, and data centers into leadership updates.

  • Chicago Eviction Risk

    Applied ML · private repository

    Built an interpretable machine-learning and geospatial workflow to explore eviction-risk patterns in Chicago.

  • Political Narrative Navigator

    Team project, University of Chicago

    Analyzed 2,000+ presidential campaign speeches alongside YouGov data using AI-assisted text workflows.

    Team repository on GitHub ↗

06 / 06

Background and contact

  • Ourea Energy — Agentic AI Intern · 2026–
  • Financial Health Network — Research Intern · 2026–
  • HOPE Lab, UChicago Booth — Research Assistant · 2026–
  • PublicPath — Co-founder, Engineering and Data · 2026–
  • PAMS — Research Associate, ML · 2025 · CERP — Analyst · 2024–25
  • MS, Computational Analysis and Public Policy, UChicago · expected June 2027
  • BS, Economics and Political Science, Lahore School of Economics · May 2024
aneesamjad@uchicago.edu GitHub LinkedIn