Backend Engineer

Responsibilities:

• Design and build the REST APIs that power the model registry, evaluation execution, and promotion workflows with clean OpenAPI specs, explicit versioning, and error handling that gives callers enough information to recover.
• Own the PostgreSQL data layer: schema design with proper indexing and constraints, migration strategy, and query optimisation that holds up as data volume grows.
• Build the async job infrastructure: Celery workers on Redis, Airflow DAGs for orchestration, and make them sufficiently observable that on-call is not a guessing game. Design and maintain the event-driven components (Kafka / Pub/Sub) with the reliability properties that distributed systems demand: idempotency, backpressure handling, dead letter queues, and retry logic.
• Ensure every service is containerised, deployable on Kubernetes (GKE), and ships with the logging, metrics, and tracing that lets you debug a production incident in under thirty minutes.
• Write architecture decision records for non-obvious choices and API documentation that a new team member can follow without needing to ask you to walk them through. ''

Requirements:

• You have shipped Python backend services to production and can point to specific decisions: schema design, caching strategy, async pattern that you made and stand behind.
• You understand PostgreSQL well enough to look at a slow query, explain why it is slow, and fix it, not just add an index and hope.
• You have built or maintained an event-driven system (Kafka, Pub/Sub, or equivalent) and can articulate what happens when a consumer falls behind or a message is processed twice.
• You understand the difference between synchronous and asynchronous failure modes and know when to use Celery, when to use Airflow, and when neither is the right answer.
• You treat observability as part of the feature, not a follow-up ticket; your services ship with structured logs, metrics, and health endpoints from day one.

Good to Have:

• Production experience with Go (goroutines, channels, context propagation). We use it for performance-sensitive microservices and would like the overlap.
• Hands-on experience with Airflow or a comparable orchestration framework for multi-step ML or data workflows.
• Familiarity with distributed tracing (Jaeger, OpenTelemetry) and a full observability stack (Prometheus, Grafana, Loki).
• Experience in a compliance-conscious environment (SOC2 HIPAA, GDPR); awareness of what needs to be auditable and why.
• This is not a role for someone who considers deployment someone else's problem. If you draw a hard line between writing code and running it, the scope here will be uncomfortable.
• Technology Area Required Preferred Languages: Python (FastAPI, async/await, Pydantic), Databases: PostgreSQL (schema, indexing, migrations), Go (goroutines, channels), Firestore, MongoDB.