Embedded Infrastructure Engineer, Chanakya

Design and operate data storage architectures (relational, document, vector, object storage) capable of managing terabyte-scale datasets across multiple modalities.

Build and maintain ingestion pipelines that reliably process daily data influx — including batch and streaming workloads — with monitoring, error handling…

Implement indexing, partitioning, and query optimisation strategies that allow AI systems and data scientists to retrieve and reason over large datasets with acceptable latency.

Work with Embedded Data Scientists to translate ontologies, schemas, and semantic structures into performant physical data models and storage configurations.

Direct experience managing multi-terabyte data stores — you have personally built or operated systems handling 10TB+ of persistent data and sustained high-throughput ingestion.

Deep working knowledge of at least two of: PostgreSQL, MongoDB, Elasticsearch, ClickHouse, or comparable systems — including tuning, indexing, partitioning, and operational management.

Experience building production data ingestion pipelines using Apache Kafka, Apache Spark, Airflow, Flink, dbt, or equivalent frameworks.

Strong proficiency in Python and/or Go, with experience writing production infrastructure tooling and automation.