/ about

Engineer, builder, researcher in training.

I work on data systems for text-heavy domains: ingestion, structure, retrieval, evaluation, and the operational discipline needed to keep those systems trustworthy.

Current profile

I am a data engineer based in Lahore, currently leading the data layer at DigiLawyer. My work sits between backend data infrastructure and applied information retrieval: turning fragmented legal sources into structured, searchable, and AI-usable records.

The common thread across my projects is reliability under messy real-world constraints: scanned PDFs, inconsistent metadata, changing source formats, production deadlines, and users who need answers they can verify.

Looking for

Data engineering, retrieval, backend, and AI infrastructure roles; graduate programs and scholarships aligned with information retrieval, NLP, data systems, or legal/public-sector technology.

Degree

BSc Computer Science, University of Engineering and Technology Lahore

Focus

Data engineering, information retrieval, NLP systems, databases, and cloud infrastructure

Final-year project

MyContract: AI-assisted contract generation, review, and compliance checking; later adapted into DigiLawyer's Mike AI Draft feature.

Research direction

Questions I want to study further.

  • Hybrid legal retrieval: lexical, dense-vector, and re-ranking systems for statutes and judgments.
  • Temporal legal data models: representing amendments, versions, and section-level provenance.
  • Reliable data infrastructure for public-interest corpora where correctness and traceability matter.
  • Evaluation methods for retrieval-augmented systems: recall, citation coverage, latency, and regression tests.
Technical strengths

What I can contribute now.

  • Production data systems: ingestion, validation, indexing, and operational recovery.
  • Search and retrieval: PostgreSQL FTS, pgvector, MeiliSearch, Qdrant, hybrid scoring, and corpus design.
  • Applied AI engineering: LLM-backed workflows where outputs need citations, structure, and reviewability.
  • Leadership in small teams: documentation, handoff discipline, intern supervision, and data quality ownership.