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, 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

• 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 — 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

• 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)

• Experience with vector databases or embedding stores (Milvus, Weaviate, Qdrant, pgvector, or similar)

• Experience deploying and operating infrastructure in air-gapped, on-premise, or hybrid environments

Note: We are looking for people who can own the outcomes described here, not

people who match every line of this specification. If this problem excites you and

you believe you can do this work; we want to hear from you.

Sarvam is a fast-moving, high talent-density team building full-stack AI for India, working on problems that push the frontiers of AI with real population-scale impact.

• Work alongside researchers, engineers, builders, and business leaders who move fast and hold each other to a very high bar

• High ownership and high impact, from day one

• Everything we do is AI-first, from the way we build and ship to the way we think about problems

• You can work on problems that could change how an entire country learns, works, and communicates

If you want to work on problems at the frontier of AI in India, Sarvam is the place to be.