Embedded Infrastructure Engineer

About the role

Embedded Infrastructure Engineers design, build, and maintain the data infrastructure that powers AI system deployments at client sites. You will work with Embedded Data Scientists and Strategic Deployment Engineers to ensure terabyte‑scale datasets are ingested, stored, queried, and served reliably.

Key responsibilities

• Design and operate storage architectures (relational, document, vector, object) for multi‑modal terabyte‑scale datasets.
• Build and maintain robust ingestion pipelines (batch and streaming) with monitoring, error handling, and back‑pressure management.
• Implement indexing, partitioning, and query optimisation to meet latency requirements for AI reasoning.
• Deploy and manage databases, vector stores, and search infrastructure in air‑gapped or on‑premise environments.
• Provide observability, capacity planning, and scaling for data platforms across client deployments.

Required profile

• 4–8 years of experience in data infrastructure, platform engineering, or site reliability engineering at large data scale.
• Hands‑on experience managing multi‑terabyte data stores (10 TB+) and high‑throughput ingestion pipelines.
• Proven ability to tune and operate systems such as PostgreSQL, MongoDB, Elasticsearch, or ClickHouse.

Required skills

• Python and/or Go for production tooling.
• Apache Kafka, Apache Spark, Airflow, Flink, or dbt for data pipelines.
• Object storage (S3/MinIO) and columnar formats (Parquet, ORC).
• Containerisation and orchestration (Docker, Kubernetes).
• Infrastructure‑as‑code (Terraform or similar).
• Familiarity with vector databases (Milvus, Weaviate, Qdrant, pgvector) is a plus.