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.