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. 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 We offer great career growth, ESOPs, Gratuity, PF and Health Insurance.