Entry-level AI/MLOps engineer focused on the operational side of ML — drift detection, model lifecycle automation, and AI-assisted incident response.

I'm based in Pakistan, and hold a Level 6 Diploma in Artificial Intelligence Operations (Al Nafi International Colleges, 2026 — scored 90%). I learn by shipping complete systems rather than tutorials, which is why everything below ships with tests and real numbers attached.

• Currently targeting AI Engineer / AIOps Engineer roles
• Building a Full Indie Fighting game with a 70+ Character roster.
• Open to remote work and relocation to the UAE, Saudi Arabia, or Qatar
• Currently deepening LangGraph (human-in-the-loop workflows) and a PyTorch LoRA fine-tuning project
• Foundation: 200+ hands-on commits across DevOps CI/CD, Red Hat Linux, and Cybersecurity labs

End-to-end MLOps platform: MLflow-tracked training with automated champion/challenger promotion, drift detection via PSI, KS, and JS divergence, a FastAPI serving layer, and a Streamlit monitoring dashboard — shipped with three GitHub Actions CI/CD workflows.

Python MLflow FastAPI Streamlit GitHub Actions Docker 34 unit tests · F1 0.87 · AUC 0.94

Isolation Forest anomaly detection feeding a rule engine, triage engine, and notification router, with a live Rich terminal dashboard — fully containerized. Extended by incident_copilot: a Chroma-backed postmortem knowledge base with hybrid TF-IDF + dense retrieval (RRF), two CrewAI agents, a Streamlit human-review UI, Kubernetes manifests, and an 8-case LLM-as-judge evaluation harness.

Python CrewAI ChromaDB FastAPI Kubernetes Docker 31 unit tests

A self-updating job board reading directly from the Greenhouse, Lever, Ashby, and SmartRecruiters APIs (plus RemoteOK, We Work Remotely, and Remotive), refreshed every ~20 minutes by a scheduled GitHub Actions workflow and published to a live GitHub Pages dashboard. Built to run my own job search — no scraping, no middleman board.

Python GitHub Actions GitHub Pages REST APIs 476 commits · full parser + integration test suite

Core

ML / AI Engineering

Also applied: Retrieval-Augmented Generation (RAG) · Hybrid Retrieval · Agentic Workflows · Prompt Engineering · LLM-as-judge Evaluation

Backend & Infra

Cloud