Systems & Data Infrastructure Engineer

Functional Focus: Data Engineering, Real-Time Streaming, and HPC Job Orchestration

• Scientific Data Architecture: Design crash-safe, binary data formats capable of handling high-speed incremental writes from compute jobs and concurrent reads for query-time calculations.
• Operational Data Modeling: Design and maintain a relational database schema via an async ORM, backend-agnostic across database engines.
• HPC Workload Management: Manage the lifecycle of batch and interactivecompute jobs, handling subprocess monitoring, status polling, and cluster filesystem coherency.
• Real-Time API & Streaming: Build async backend services and long-lived, server-pushed event channels with backpressure handling, and enforce role-based access control on all APIs.
• AI Service Integration: Integrate and orchestrate calls to an AI/analytics service from the backend, coordinating with application state.

Requirements

Mandatory:

• Python 3, async/await
• FastAPI (or similar async Python web framework)
• Relational DB modeling, async ORM (e.g. Tortoise, SQLAlchemy), SQL
• Server-Sent Events or WebSockets, backpressure handling
• Role-based access control (RBAC), token-based auth (JWT/OAuth)
• Subprocess management, batch job lifecycle/status polling
• Binary/streaming file format design, crash-safe writes
• REST/SDK integration with an external AI/LLM service

Optional:

• Go or another async-capable backend language
• GraphQL
• PostgreSQL administration, Alembic/migrations tooling
• Message queues/brokers (Redis, RabbitMQ, Kafka)
• Air-gapped/offline deployment experience
• HPC schedulers (SLURM, PBS), Linux cluster filesystems
• NumPy/columnar formats (Parquet, HDF5)
• Tool-calling / function-calling orchestration patterns