Embedded Infrastructure Engineer, Chanakya
Delhi • FullTime
Posted 5mo ago
About the job
Embedded Infrastructure Engineers at Sarvam design, build, and maintain the data infrastructure essential for deploying AI systems at client sites. You will collaborate with Embedded Data Scientists and Strategic Deployment Engineers to ensure the reliable and performant ingestion, storage, querying, and serving of terabyte-scale datasets to AI reasoning engines. This involves constructing and managing data platforms capable of handling large volumes of structured records, documents, imagery, audio, and geospatial data, including databases, object stores, ingestion pipelines, and processing layers. You will be responsible for making key decisions regarding storage architecture, indexing strategies, pipeline orchestration, and system performance, often in challenging environments like air-gapped or operationally sensitive settings that preclude the use of standard cloud services or enterprise tooling. Ultimately, you will own the reliability and performance of the infrastructure layer for your assigned accounts.
Responsibilities
- Design and operate data storage architectures for terabyte-scale datasets across multiple modalities.
- Build and maintain ingestion pipelines for batch and streaming workloads with monitoring and error handling.
- Implement indexing, partitioning, and query optimization strategies for efficient data retrieval by AI systems.
- Translate ontologies, schemas, and semantic structures into performant physical data models.
- Deploy and manage database systems, vector stores, and search infrastructure in constrained environments.
- Build observability into the data platform to monitor pipeline health, storage utilization, and performance.
- Own capacity planning and scaling decisions for data infrastructure.
- Collaborate with product and engineering teams to integrate infrastructure learnings into the core platform.
Requirements
- 4-8 years of experience in data infrastructure, data engineering, platform engineering, or SRE.
- Direct experience managing multi-terabyte data stores (10TB+ persistent data and high-throughput ingestion).
- Deep knowledge of at least two from PostgreSQL, MongoDB, Elasticsearch, ClickHouse (including tuning and operational management).
- Experience building production data ingestion pipelines with Kafka, Spark, Airflow, Flink, or dbt.
- Strong proficiency in Python and/or Go for infrastructure tooling and automation.
- Solid understanding of storage systems and formats (object storage, columnar formats).
- Familiarity with containerization and orchestration (Docker, Kubernetes) in production.
- Experience with infrastructure-as-code and deployment automation (Terraform or similar).