Pranav AroraApplied AI ScientistRésumé ↗

Background & practice

The work behind the work.

Six years across analytics, manufacturing, enterprise software, and applied AI.

Pranav Arora

About me

From factory floors to applied AI.

I started in analytics at Dentsu, worked on manufacturing systems at Micron, and built enterprise AI at Hewlett Packard Enterprise. Today I'm an Applied AI Scientist at Inception42 in Abu Dhabi.

That path shapes how I work: understand the operating problem, build a measurable experiment, and make the result usable by the people who depend on it.

Moving from Singapore to Abu Dhabi opened a new chapter: agentic systems, bilingual workflows, and the evaluation needed to put them into use.

Experience

Where I've done the work.

Applied AI, enterprise software and machine learning inside systems where reliability and measurable outcomes matter.

01

Inception42 · a G42 company

Applied AI Scientist

Jul 2026 - PresentAbu Dhabi, UAE
  • Machine-Readable Government: turning uploaded government policy into machine-readable artifacts that drive evidence-backed, reviewable case workflows
  • Bilingual PDF-to-markdown and policy-graph processing with governed extraction review, versioned provenance and controlled publication
  • Eval Workbench: approved ground-truth suites, repeated-run stability measurement and exact-version release gates before anything ships
  • Agentic case processing on FastAPI, PostgreSQL and AKS, with row-level security and immutable run fencing
02

Hewlett Packard Enterprise

Senior ML Engineer

Aug 2024 - Jul 2026Singapore
  • Text-to-SQL platform: 85% accuracy, 2,000+ queries/week across 7 business units
  • K8s Watcher agentic system: 70% MTTR reduction, 50+ incidents/week
  • Document Planning Hub: LangGraph multi-agent, 5,000+ users, 80% error reduction
  • OneAI platform standards across 8 teams: deployment failures down 60%
03

Micron Technology

Data Scientist

Jan 2022 - Aug 2024Singapore
  • PPO RL wafer scheduling: $10M annual revenue impact, 0.5% production increase
  • Predictive maintenance pipeline: 30% downtime reduction across 70-machine cluster
  • LLM fine-tuned on 10K internal docs: 80% first-contact resolution, BLEU 0.82
04

Dentsu International

Data Scientist

Aug 2020 - Jan 2022Singapore
  • ROAS prediction models: 50% faster post-campaign analysis, 20% cost reduction
  • Customer propensity model: 85% validation accuracy, deployed to live campaigns
  • Data catalog on Azure AKS: ingesting 10,000+ datasets for enterprise governance

Education

  • MSc IT in Business, Artificial Intelligence

    Singapore Management University

    Jan 2019 - May 2020

  • BTech Computer Science

    University of Petroleum and Energy Studies, India

    Aug 2014 - Apr 2018

Tools and practice

Practice, backed by projects.

The tools I use, with examples you can inspect.