Data Scientist

Responsibilities:
Design and build evaluation frameworks for Sarvam's AI outputs across domain-specific requirements: document comprehension, command summarization, geospatial reasoning, enterprise workflow automation, and others as they emerge.
Define quality metrics in collaboration with domain experts and clients; translate operational requirements into measurable, defensible signals.
Run structured evaluation cycles pre- and post-deployment; build dashboards that surface model quality in production.
Identify failure modes, edge cases, and distribution shifts with the bias of someone looking for what's wrong, not confirming what's right.
Collaborate with the MLOps Engineer to operationalize evaluation pipelines automated, triggered by deployment events, versioned, and reproducible.
Build and manage domain-specific datasets for fine-tuning, evaluation, and benchmarking, including human annotation workflows where needed.
Publish internal findings and quality reports that feed the product and engineering roadmap.
Requirements:
3-6 years in data science, ML research, or applied AI; at least 2 years working with LLMs in production contexts.
Strong statistics and probability fundamentals you understand what makes an evaluation valid and what makes it misleading.
Experience designing evaluation frameworks from scratch: custom metrics, inter-rater reliability, and red-teaming methodologies.
Python proficiency; comfort with pandas, NumPy, Hugging Face datasets, RAGAS, EleutherAI Eval Harness, LangSmith, or equivalent.
Experience with prompt engineering, model fine-tuning, or RLHF in applied settings.
Ability to work with unstructured domain data: PDFs, doctrine documents, transcripts, and field reports.

Experience
3-6 yrs