Embedded Data Scientist, Chanakya

About this job

Sarvam AI is building India's full-stack sovereign AI platform, focusing on research, models, infrastructure, and applications to make AI work for the country. As an Embedded Data Scientist for the Chanakya role based in Delhi, you will transform complex client data into structures that AI systems can reliably reason over. Deployed alongside Strategic Deployment Engineers at client sites, you will work directly with client data environments to understand, structure, and operationalise large-scale datasets. A normal day involves working with heterogeneous, multimodal data such as documents, images, audio, geospatial data, and structured records. You will design semantic structures, define how data is represented inside AI systems, create ontologies, tagging systems, and knowledge graph structures, and own the quality of the data layer to ensure reliable reasoning at scale.

Responsibilities

• Understand client data landscapes across documents, imagery, audio, geospatial data, and structured records.
• Design domain ontologies representing entities, relationships, hierarchies, and operational concepts.
• Define document segmentation and chunking strategies that preserve semantic meaning.
• Work with heterogeneous datasets and define how different modalities should be indexed, embedded, and linked.
• Collaborate with Strategic Deployment Engineers to translate semantic structures into operational data ingestion pipelines.
• Evaluate AI system retrieval and reasoning performance, refining structures for improvement.
• Collaborate with model teams to define benchmarks and evaluation criteria reflecting real-world conditions.
• Translate insights from client data environments into structured signals for product and engineering teams.

Requirements

• 2 to 5 years of experience in data science, applied machine learning, or large-scale data analysis roles.
• Strong Python skills including pandas, NumPy, and modern NLP or LLM tooling.
• Solid grounding in machine learning fundamentals to understand model behavior and contribute to evaluation design.
• Experience working with large unstructured datasets including documents, transcripts, reports, or operational records.
• Familiarity with LLM-based systems, retrieval pipelines, or vector search systems.
• Experience designing or working with data schemas, metadata frameworks, entity models, or semantic data structures.

Good to have

• Familiarity with knowledge graphs, ontologies, or semantic data modelling.
• Experience with multimodal datasets including text, imagery, audio, geospatial, or structured data.
• Experience operating in constrained or air-gapped environments.

Who should apply

This role suits professionals with two to five years of experience in data science, applied machine learning, or large-scale data analysis. Candidates should be comfortable operating autonomously in client environments, possess strong Python and NLP skills, and have experience handling messy, unstructured datasets. You should be able to design structures from scratch and communicate effectively with engineers and client stakeholders. Please note that this posting does not mention whether freshers can apply.

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