Pranav Arora
ML engineer shipping production AI for six years. RL schedulers moving $10M of wafer fab output, multi-agent platforms serving 5,000+ people.
Singapore, relocating to Abu Dhabi
Research into running systems
Six years across semiconductor manufacturing, enterprise AI and digital marketing. The pattern is the same every time: take the paper, ship the system, prove the number.
"I want to work on AI systems that operate at civilisational scale."
That is why I am moving to the UAE. From RL agents scheduling wafer fab runs at Micron to LangGraph multi-agent platforms at HPE, the work I care about is the unglamorous middle: evaluation, deployment, observability, the parts that decide whether AI actually works in production.
Off the clock: photography, chess, quizzing, and an unreasonable number of Marvel rewatches.
The toolkit
Full lifecycle: fine-tune the model, deploy it on Kubernetes, watch it in production. The first two in each row are where I go deepest.
The route so far
Senior ML Engineer
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%
Data Scientist
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
Data Scientist
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
Singapore chapter complete. Abu Dhabi next.
Built in public
Open, deployed and inspectable. Production engineering depth you can read the source of, not toy demos.
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.
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.
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.
More at github.com/Pranav63
Pull up to the fire
The sun set somewhere around the experience section. If you are hiring for applied AI in the UAE, or have a production AI problem worth talking about, this is the place.
Relocating to Abu Dhabi. Open to Applied AI, ML Engineering and Research Engineering roles.