1. Role Summary

Oncoshot turns unstructured clinical documents (clinical notes, pathology, radiology and genomic
reports) into a structured, research-grade record of each institution’s cancer population:
diagnoses, histology, TNM and overall stage, biomarkers, treatments and disease status. This
extraction runs on open-weight large language models deployed inside each hospital’s own
environment, because patient-derived content cannot leave the institution. The Applied AI Engineer
(LLM Extraction) designs, builds and improves these extraction pipelines, and is accountable for the
measured accuracy of the data they produce.

This is a hands-on engineering role, not a research position: turning oncology concepts into
precise extraction tasks, engineering prompts, schemas and pipelines around models we host
ourselves, and proving every change with numbers rather than examples. To see how we measure
extraction quality, look at our open-source evaluation framework, llmvalidate
(https://github.com/Oncoshot/llm-validation-framework), which this role works with and extends.





We are hiring at senior level by preference, and we welcome mid-level and early-career candidates
who show solid fundamentals and want to grow into the role. We invest in training, including the
clinical and oncology domain knowledge. The level and salary are set at offer, based on assessment.
2. Key Responsibilities
- Own extraction pipelines end to end: For each clinical document type, from classification and
chunking through structured extraction and normalisation to clinical coding standards, with a
confidence score and source justification for every extracted field.
- Engineer for open-weight models: Select, benchmark, serve and optimise LLMs that run on
hardware the hospital controls, balancing accuracy against latency and GPU cost. Decide where a
rule, lookup or curated vocabulary does the job better than a model call.
- Let the numbers decide: Evaluate every change against gold-standard dataset

📌 Applied AI Engineer (Thane)
🏢 Oncoshot India
📍 Thane