I build AI products that have to work in the real world.
Right now I'm focused on a mix of applied AI, product architecture, and technical leadership across privacy-sensitive, regulated, and high-stakes environments. Most of the work I care about sits at the intersection of product usefulness, model reliability, and infrastructure decisions that won't become a liability later.
- Building privacy-first AI products with a strong bias toward practical use, clean UX, and sustainable architecture
- Designing retrieval systems that are more deterministic, better scoped, and less prone to hallucination
- Shipping multimodal workflows that combine text, documents, visuals, and structured data
- Exploring sovereign and EU-hosted AI stacks for teams that care about compliance, residency, and vendor risk
- Turning messy operational problems into decision systems, simulations, and tools people can actually use day to day
- Growing small software products from idea to usable product without overengineering the first version
These are the kinds of problems I'm deep in at the moment:
- Knowledge retrieval tools with hard guardrails, filtered context, and evaluation loops
- AI-assisted systems for document-heavy and research-heavy workflows
- Computer vision workflows for messy real-world environments
- Decision-support systems that use causal thinking instead of just prediction
- Lightweight SaaS products with strong product sense and careful cost control
- Architecture reviews for early-stage products figuring out what to build in-house vs what to outsource to vendors
- RAG systems
- multimodal AI applications
- privacy-aware AI workflows
- internal tools that save teams real time
- MVPs that can grow into actual products
- evaluation layers for LLM-based systems
- infrastructure setups that stay understandable six months later
Python TypeScript SQL PostgreSQL Docker PyTorch scikit-learn BigQuery Airflow
Also spending a lot of time around:
RAG multimodal systems causal inference computer vision Mistral Gemini AWS GCP OVHcloud Scaleway
- start from the actual business bottleneck
- reduce ambiguity early
- build small, verify fast
- keep AI systems observable
- avoid magic where reliability matters
- write things down so future decisions are easier
I’m especially interested in:
- AI products with real operational value
- European AI infrastructure and sovereignty
- product strategy for small technical teams
- the gap between demo AI and deployable AI
- Website: arubhardwaj.eu
- LinkedIn: linkedin.com/in/arub
- GitHub: github.com/arubhardwaj