Embedded Infrastructure Engineer, Chanakya

About the role

Embedded Infrastructure Engineers design, build, and maintain the data infrastructure that underpins AI system deployments at client sites. You work alongside Embedded Data Scientists and Strategic Deployment Engineers to ensure that terabyte-scale datasets can be ingested, stored, queried, and served to AI reasoning engines reliably and performantly.

What you'll do

• 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…
• Work with Embedded Data Scientists to translate ontologies, schemas, and semantic structures into performant physical data models and storage configurations
• Deploy and manage database systems, vector stores, and search infrastructure in air-gapped, on-premise, or security-constrained environments

What you need

• Python
• Terraform
• PostgreSQL
• MongoDB
• AWS
• Elasticsearch

Good to have

• Kafka
• Spark
• Airflow
• dbt
• Go
• Docker