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A to Z

The longer version

How I got into applied AI, what I have shipped, and what I am still working on.

Draft: needs your words

How I got here

  • What pulled you into AI/ML in the first place: a project, a course, a problem you hit?
  • Why applied engineering over pure research?
  • Anything before Tech Wing's Lab worth mentioning: freelance work, competitions, self-taught stretches.

Education

BSc in Computer Science and Engineering

American International University, Bangladesh

GPA 3.89 · Graduated May 2026

Coursework: Artificial Intelligence, Machine Learning, Data Science, Data Analytics, SQL.

Work history

Qualfix

Dhaka, Bangladesh

AI Engineer

Current

January 2026 - Present

  • Built an email automation agent in LangGraph that classifies incoming sales emails and extracts structured records from PDFs, images, and spreadsheets in a single Gemini 2.5 Pro call, with the output schema enforced by Pydantic.
  • Wired up the observability stack around it: MLflow 3.x for per-email traces and token cost, Prometheus + Grafana for queue depth, p95 latency, and monthly billing. Runs on FastAPI and async PostgreSQL inside Docker Compose.
  • Deployed and configured Odoo 19 Community as the internal management and CRM system across two legal entities on self-hosted Docker infrastructure via Dokploy.
  • Led an AI agent-assisted migration of 14 years of historical business data from Salesforce into Odoo, completed in 4 weeks.
  • Building a separate POC for voice agents handling inbound and outbound calls; currently in review.

AI Engineer (Remote, part-time)

July 2025 - December 2025

  • Took an internal customer-matching system from 37% to about 88% accuracy by reworking the matching logic and cleaning the underlying data.
  • Cleaned and structured contact data for medical organisations before loading into Postgres for downstream queries.

Tech Wing's Lab

Remote · Dhaka, Bangladesh

AI/ML Researcher

December 2024 - June 2025

  • Co-authored a peer-reviewed paper on phishing website detection (ICERIE-25). Random Forest with TF-IDF and Count Vectorizer features performed best on the imbalanced dataset.
  • Drafted deep-learning architectures (RNN/LSTM) for real-time detection as a follow-up direction.

Research

Phishing website detection

Peer-reviewed · ICERIE-25 · co-author

Random Forest with TF-IDF and Count Vectorizer features performed best on the imbalanced dataset. Follow-up work drafted RNN/LSTM architectures for real-time detection.

Certifications

Draft: needs your words

How I work

  • What you optimise for when building: reliability, cost, speed of iteration?
  • How you decide between a heavier model and better data (the 37%→88% story is a good example).
  • What you want to be doing more of in the next couple of years.

Draft: needs your words

Outside work

  • Interests, communities, side projects, anything that makes you a person rather than a CV.
  • Optional: delete this block if you would rather keep the page strictly professional.

Get in touch

Email is the best way to reach me: lutfulkabir1757@gmail.com

AI Engineer @ Qualfix, building data pipelines, voice AI agents, and ERP-integrated automation.

lutfulkabir1757@gmail.com

Address

Dhaka, Bangladesh

lutfulkabir1757@gmail.com

+8801571337605

© 2026 Lutful Kabir