Results first, then how they happened.

Five pieces of work, each written the same way: the problem, what I did, what changed. The numbers are the ones I can stand behind. Where the data is private, the source says so.

40.9% of meetings in the morning, against a 30–35% peer target internal data · methodology on request

Course-scheduling study, NYIT

Graduate Research Assistant, Data Science · Jan 2025 – Jun 2026 · [with Dr. Cecilia Dong — confirm ok to name]

Challenge
The department needed a factual picture of how sections, meeting times, and enrollment were actually distributed across the schedule, and where that diverged from peer targets. [confirm framing]
Action
Cleaned and reconciled 15,242 raw registrar records into 8,729 sections and 11,803 meeting instances, then defined and computed the metrics that mattered: time-of-day share, overenrollment by format, Friday load.
Result
40.9% of meetings fell in the morning against a 30–35% peer target; 24.2% of lectures were overenrolled; 10.0% of meetings sat on Fridays. Scope: 3,300+ undergraduates. [where the findings went / what changed — fill]
MORNING MEETINGS target 30–35% 40.9% OVERENROLLED LECTURES 24.2% FRIDAY MEETINGS 10.0%
Share of class meetings — 15,242 registrar records, 8,729 sections. Bars scaled to a 45% axis.
40+ AI/ML and full-stack products shipped to clients altechra.com ↗

Production AI systems, Altechra

Founder · Apr 2025–present · New York

Challenge
Clients want LLM features that behave in production, not in a demo: predictable outputs, human review where the stakes are high, and evidence that the system works before it ships.
Action
Designed and shipped the systems end to end: RAG pipelines, LLM agents with human-in-the-loop review, tool calling and structured outputs, evaluation harnesses, and the full-stack products around them (Python, React, Node.js, AWS, Docker, CI/CD).
Result
40+ products shipped. Two anonymized examples: a SaaS product delivered with a 98-test suite, and a system processing 600+ emails per weekday across 45 mailboxes. Client names stay private.
85% résumé–job matching accuracy github.com/Zulqarnain-10/JobCraft ↗

JobCraft

Personal project · Jun–Sep 2025 · open source since Aug 2026

Challenge
Keyword search misses the real overlap between a résumé and a role, so job seekers wade through listings that were never a fit.
Action
Built a multi-agent system that reads the résumé and the posting and scores fit through semantic matching over vector embeddings with NLP scoring (Python, LangChain, OpenAI API), then containerized it on AWS EC2 with Docker and GitHub Actions CI/CD.
Result
85% résumé–job matching accuracy. Released the code publicly in Aug 2026.
94% query accuracy on the evaluation set GitHub ↗

MedBot

Personal project · Feb–May 2025

Challenge
A medical Q&A assistant that guesses is worse than none. Answers had to be grounded and measurable.
Action
Built a retrieval-grounded (RAG) medical question-answering assistant: trusted medical PDFs embedded into a Pinecone vector knowledge base with Sentence Transformers, orchestrated with LangChain, and served through a Flask app.
Result
94% query accuracy on the evaluation set, scored with precision, recall, and F1.
+15% revenue, with reporting time cut by 30% internal · details on request

Operations analytics, NSK

Data & operations analytics · part-time · Aug 2024–present · petroleum wholesale, Brooklyn

Challenge
Pricing, sales, and day-to-day reporting were manual, so trends and problems surfaced late.
Action
Built the pricing and sales analysis layer over operations data (Python, Excel), automated the recurring reports, and turned the findings into KPIs the owners now track.
Result
Pricing and sales trends contributed to a 15% revenue increase; automated reporting cut manual processing time by 30%. Ongoing.

More builds

  • Material classification · CNN over fused sensor data, PEC-funded FYP96% accuracyGitHub ↗
  • Disease prediction modelGitHub ↗
  • Credit scoring modelGitHub ↗
  • Handwritten character recognitionGitHub ↗
  • Behavioral risk factor analysis, tobacco useGitHub ↗
  • Data science mastery, exploration to predictionGitHub ↗

Stack

What I work with

Analysis & modeling

  • Python
  • SQL
  • PyTorch
  • scikit-learn · pandas
  • Statistics

LLM systems

  • RAG · LangChain · LangGraph
  • Hugging Face
  • Tool calling · structured outputs
  • Evaluation harnesses
  • Human-in-the-loop review

Shipping

  • AWS · Docker
  • CI/CD · Git
  • Flask · Streamlit
  • React · Node.js

Listed only what I have used on shipped work. [A/B testing, causal inference, forecasting, Power BI, MLflow/DVC, SageMaker: add here only after the matching gap project ships — see 04-gap-project-plan]

Founder & CEO

I also run an agency — meet Altechra.

I founded Altechra to bridge clients with world-class engineering talent. We have delivered 40+ products — SaaS platforms, AI systems, fintech and mobile apps — with me leading discovery, architecture, and delivery end to end.

It's where my data science meets the real world: scoping ambiguous problems, managing teams, and being accountable for outcomes, not just models.

Visit Altechra ↗ Start a project
40+products delivered across web, mobile & AI
E2Ediscovery → architecture → deployment → support
5★Upwork-verified client reviews on altechra.com

Contact

Building something with data? Say hi.

A role, a collaboration, or a question about anything on this page — my inbox is open, and email is the fastest way in. No deck required.

zulqarnainhsyed@gmail.com
Résumé (PDF) LinkedIn GitHub Upwork