ABOUT PARAMAIQ & NYAAYOS

ParamAIQ is an applied-AI company building NyaayOS, the reasoning layer for Indian law. India runs one of the largest judicial systems on earth: more than 55 million pending cases and one of the largest legal workforces anywhere. Yet legal expertise stays concentrated in a handful of senior counsel and metro firms.

For a litigant in a district court, often arguing in a regional language, the outcome can turn on who they can afford. NyaayOS turns the primary sources of Indian law (the Bharatiya Nyaya Sanhita, the BNSS and BSA, decades of Supreme Court and High Court precedent, statutes, and gazettes) into a reasoning layer any advocate can stand on. Work that took days takes minutes, so the bottleneck shifts from access back to advocacy.

We give an independent lawyer the same analytical leverage as a partner at a leading firm, and we build it with practising lawyers, not just for them.

Generating text is the easy part. The hard part is making sure every sentence we put in front of a lawyer rests on real, currently valid authority. That is the problem you own. You own how NyaayOS represents legal knowledge, and how it finds and verifies the law behind an answer.

Indian law is vast, cross-referenced, and always moving statutes get amended, judgments get overruled, positions shift. Turning that into something a machine can search, reason over, and stan behind is the core research problem of the company. You settle it with experiments against a legal eval set you build with our in-house lawyer, not with intuition.

The work ships into a product lawyers rely on, and it is judged there.

WHAT YOU'LL OWN

- How we represent a large, changing body of law so a machine can reason over it and know what is currently authoritative.
- How we retrieve the right law with precision, where the answer can turn on a single fact, provision,



or jurisdiction.
- How we verify that an answer is grounded in a real source that is still valuable law and catch it when it isn't.
- The legal eval set as the source of truth. You design the benchmarks and you own the number.
- The research direction. On a small team, you frame the problem, run the experiment, and make the call.

THE HARD PROBLEMS YOU'LL TAKE ON

- Representing law that changes under you. An authority that governs today can be overruled
- tomorrow, and the representation must know the difference.
- Proving groundedness at scale. A fluent answer citing a real sounding but wrong or dead authority is worse than no answer. Verifying grounding reliably is unsolved in this domain.
- Defining "correct" where experts disagree. Before you can measure a system, you must build ground truth in a field where correctness is argued, not given.

WHAT WE LOOK FOR

- Strong command of modern retrieval and representation: dense and sparse embeddings, vector
- and lexical search, rerankers, and when each win. You can build and evaluate a retrieval system end to end.
- Hands-on at the model layer for RAG and reasoning: prompting, structured extraction, and using
- LLMs as components of a larger system.
- Knowledge representation in practice: knowledge graphs, entity and relationship modelling, and
- using structure inside search and reasoning.
- Eval-driven for real: you design benchmarks, build datasets, and measure factuality and
- groundedness, not just offline accuracy.
- Comfortable where learned models meet symbolic or rule-based logic.
- Strong Python and rigorous experiment design, and you write clearly enough that an engineer can build from your spec.

BONUS POINTS

- A high-stakes domain where being wrong has real consequences: legal, medical, financial.
- Indian law, or Indian-language NLP.
- Information extraction and NER from messy documents.

📌 ML Research Scientist (Mumbai)
🏢 ParamAIQ
📍 Mumbai