Embedded Infrastructure Engineer, Chanakya
DelhiFull-timePosted 3mo ago
About the role
Embedded Infrastructure Engineers design, build, and maintain the data infrastructure that underpins AI system deployments at client sites, handling terabyte-scale datasets across multiple modalities while operating in air-gapped or security-constrained environments.
Responsibilities
- 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, and backpressure management
- 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
- Deploy and manage database systems, vector stores, and search infrastructure in air-gapped, on-premise, or security-constrained environments
- Build observability into the data platform: monitor pipeline health, storage utilisation, query performance, and ingestion lag
- Own capacity planning and scaling decisions for data infrastructure across assigned client deployments
- Collaborate with product and engineering teams to feed infrastructure learnings back into the core platform and tooling
Qualifications
- 4–8 years in data infrastructure, data engineering, platform engineering, or site reliability engineering, ideally at organisations operating at significant data scale
- Direct experience managing multi-terabyte data stores — 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
- Solid understanding of storage systems and formats: object storage (S3/MinIO), columnar formats (Parquet, ORC), and how to choose the right storage layer for the workload
- Familiarity with containerisation and orchestration (Docker, Kubernetes) in production settings
- Experience with infrastructure-as-code and deployment automation (Terraform or similar)
Skills
Required:Data storage architecturesData ingestion pipelinesQuery optimization and indexingDatabase/vector storesObservabilityCapacity planningInfrastructure tooling
Nice to have:Ontology/schema translation
Last updated Jul 14, 2026. Interested? Apply on the company site.
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