Based in Singapore · Open to UAE

Pranav Arora

View My WorkResume
Scroll to explore
Who I am

About Me

PA
Pranav Arora
Senior ML Engineer · Singapore

ML & AI engineer with 6+ years building production systems across semiconductor manufacturing, enterprise AI, and digital marketing. I specialise in turning complex research into deployed, measurable systems — from RL agents scheduling $10M wafer fab runs to LangGraph multi-agent platforms serving 5,000+ users.

Currently building towards applied AI roles in the UAE — where I want to work on AI systems that operate at civilisational scale.

PhotographyMarvel FilmsQuizzingChess
0+
Years in Production AI
$0M
Revenue Impact (Micron RL)
0+
Users Served (HPE DPH)
0%
Query Accuracy (Text-to-SQL)
Education
MSc — Artificial Intelligence
Singapore Management University · 2019 – 2020
B.Tech — Computer Science
UPES, Dehradun · 2014 – 2018
Business Analytics
Harvard Business School Online · 2020
What I work with

Skills & Expertise

Full-stack production AI — from fine-tuning models to deploying agentic systems at enterprise scale.

Agentic AI & LLMs
LangGraphLangChainGPT-4oRAG SystemsMulti-Agent OrchestrationPrompt EngineeringFunction CallingLangSmithAutoGenMCP
Model Development
QLoRA / PEFTFine-tuningvLLMHuggingFace TransformersPPO / Ray RLlibPyTorchTensorFlowScikit-learnXGBoostSHAP / LIME
Evaluation & Observability
RAGASLLM-as-JudgeMLflowMTEBPrometheusGrafanaLangSmith TracingA/B TestingModel VersioningDrift Detection
Cloud & Infra
Azure AI FoundryAzure OpenAIAKSAzure DevOpsAWS (SageMaker, Bedrock, EKS)GCP (Vertex AI, BigQuery)KubernetesDockerIstioKeycloak
MLOps & Deployment
KServeBentoMLTorchServeFastAPIGitHub ActionsCI/CD PipelinesKubeflowHashiCorp VaultAzure Container AppsFly.io
Data & Vector Stores
PostgreSQL (pgvector)QdrantPineconeChromaDBRedisSupabaseSnowflakeApache AirflowAzure SynapsePrefect
Where I've worked

Experience

Senior ML Engineer
Hewlett Packard Enterprise·Singapore
Aug 2024 – Present
  • 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%
LangGraphGPT-4oAzure OpenAIKubernetesFastAPI
Data Scientist
Micron Technology·Singapore
Jan 2022 – Aug 2024
  • 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
PPORay RLlibPyTorchGCPDocker
Data Scientist
Dentsu International·Singapore
Aug 2020 – Jan 2022
  • 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
PythonSQLTableauAzureTerraform

6 years · 3 companies · Singapore

What I've built

Projects

Production-grade public projects — built to demonstrate real engineering depth, not toy demos.

● Live

Autonomous Multi-Agent Research System

LangGraph orchestration with specialized research agents

Production multi-agent system where a planner agent decomposes research queries, dispatches to specialist agents (search, summarise, critique, synthesise), and produces structured research reports. Built with LangGraph state machines, streaming responses, and full agent observability.

Live on Fly.ioLangGraph DAGStreaming Output
  • Planner → Researcher → Critic → Synthesiser agent pipeline
  • LangGraph state machine with conditional edge routing
  • Real-time streaming responses via FastAPI + SSE
LangGraphMulti-AgentGPT-4oFastAPIFly.ioPython
● Live

Production RAG + Eval Framework

Hybrid retrieval with LLM-as-judge evaluation dashboard

End-to-end RAG pipeline with hybrid BM25 + dense retrieval, HNSW indexing, cross-encoder reranking, and a RAGAS-based evaluation dashboard tracking retrieval quality drift in production.

Hybrid BM25 + DenseLLM-as-JudgeLive on Fly.io
  • Cross-encoder reranking over HNSW vector index
  • RAGAS evaluation dashboard with drift detection
  • Supabase persistence + Streamlit observability frontend
LangChainQdrantRAGASSupabaseFly.ioPython
● Live

QLoRA Multilingual Fine-tuning Pipeline

Arabic/multilingual LLM fine-tuning with MLflow + vLLM serving

QLoRA fine-tuning of Llama 3.2 3B on Arabic NLP tasks using HuggingFace PEFT. Full MLflow experiment tracking, base vs fine-tuned benchmark comparison, and vLLM serving layer for inference.

BLEU +18% vs baseMLflow TrackedvLLM Served
  • QLoRA 4-bit quantisation on Llama 3.2 3B
  • BLEU + ROUGE benchmarking vs base model
  • MLflow experiment tracking + vLLM inference server
QLoRAPEFTMLflowvLLMLlama 3.2Arabic NLP

More on github.com/Pranav63

Get in touch

Let's Build Something

Open to applied AI roles in UAE, collaborations, and interesting production AI problems.

Email
pranav2vis@gmail.com
LinkedIn
linkedin.com/in/pranavarora63
GitHub
github.com/Pranav63
Available for UAE roles

Based in Singapore · Relocating to Abu Dhabi / Dubai. Open to Applied AI, ML Engineering, and Research Engineering roles.

contact.shpranav@portfolio ~
$ name
$ email
$ msg