Senior Backend Engineer, ML Infrastructure & Reliability

Your mission

Location: Remote (CET)

Type: Full-time

About Us

Graswald AI is transforming how the world’s most iconic brands create content using AI. Backed by leading investors and powered by a world-class team, we’re redefining the fashion content process - no physical studios, samples, logistics - just cutting-edge AI and automation. We build AI systems that power large-scale content generation for global brands. Our core production application is a Django-based backend that coordinates high-throughput ML inference across many internal systems and external providers. As usage grows, reliability, orchestration, and operational correctness are critical to the business. This role exists to ensure those systems remain dependable, observable, and scalable as we grow.

Your profile

The Role

This is a backend software engineering role with end-to-end reliability ownership. You will design, build, and operate a Django production backend that orchestrates ML inference workflows across internal services and third-party APIs. The core challenge is high-throughput orchestration: asynchronous execution, retries, idempotency, backpressure, failure handling, and system-level observability. Infrastructure and Terraform are supporting tools. The primary output of this role is reliable production software. You will work closely with ML engineers and backend teams to turn research systems into robust, production-grade services.

What You’ll Do

• Design, build, and maintain Django services that coordinate and serve ML inference workflows.
• Own high-throughput asynchronous execution using queues, workers, and schedulers.
• Design safe orchestration patterns: idempotency, deduplication, retries, rate limiting, and backpressure.
• Build and operate systems with clear SLOs, error budgets, and on-call ownership.
• Lead incident response, write postmortems, and drive long-term reliability improvements.
• Implement end-to-end observability: metrics, logs, traces, dashboards, alerts, and runbooks.
• Improve reliability of service integrations using timeouts, circuit breakers, fallbacks, and dependency health modeling.
• Collaborate with ML engineers to productionize training and inference pipelines.
• Own CI/CD and deployment workflows for backend and ML-facing services.
• Use Infrastructure as Code (Terraform) to support reliability, scalability, and repeatability.
• Optimize performance and cost across compute, storage, databases, and external dependencies.

What We’re Looking For

Required

• Strong background as a Python backend engineer with ownership of production systems.
• Hands-on experience running Django in production (ORM usage, migrations, performance tuning, request lifecycle).
• Experience integrating with multiple internal and external services in reliability-critical paths.
• Proven experience building and operating asynchronous job systems (e.g., Celery, RQ, Arq, or equivalents).
• Hands-on experience with workflow or orchestration systems (Temporal, Prefect, Airflow, Step Functions).
• Solid understanding of distributed systems reliability: timeouts, retries, idempotency, rate limiting, backpressure, and failure isolation.
• Experience defining and operating SLOs/SLAs, including alerting and on-call participation.
• Strong Linux, networking, and debugging fundamentals.
• Working knowledge of cloud platforms (AWS and/or GCP).
• Practical experience using Infrastructure as Code (Terraform) as part of a broader system.

Nice to Have

• Experience operating ML inference or training infrastructure at scale.
• Familiarity with MLOps tooling (SageMaker, Vertex AI, Kubeflow, MLflow, Argo Workflows).
• Experience with distributed tracing and observability stacks (OpenTelemetry, Prometheus, Grafana, ELK/Loki).
• Experience operating Postgres and caches (e.g., Redis) in high-throughput systems.
• Startup or greenfield system ownership experience.

Role Boundaries

This role is not primarily focused on:

• Writing Terraform modules or managing clusters full-time.
• Ticket-driven infrastructure support.
• Platform enablement without production ownership.

Success is measured by reliable backend orchestration, production stability, and system-level outcomes.

Why This Role

• High ownership over core production systems that power ML inference.
• Real reliability and scale problems, not maintenance work.
• Close collaboration with backend and ML engineers.
• Opportunity to define reliability standards as the platform scales.

If you’ve owned Django services in production, built high-throughput async systems, and care deeply about reliability, this role should feel familiar.

Why us? Why Join Us

• Impact: Build and own the core infrastructure that powers AI experiences for global brands.
• Scale & Performance: Tackle challenging reliability and performance problems across training and inference.
• Autonomy: High ownership to define standards, tooling, and best practices for reliability.
• Growth: Work with a high-caliber team in a fast-scaling environment with significant career upside.
• Culture: Pragmatic, collaborative, and quality-focused engineering culture.

About us

At Graswald AI, we are building the AI operating system for fashion brands and retailers, to drive efficiency, flexibility and profitability. Today we specialise in generating eCommerce and campaign imagery and video. In just the past year, we’ve brought on 50 enterprise fashion brands, helping them reduce costs, accelerate timelines, and maintain the highest standards of visual quality. Backed by leading VCs and strategic investors - including Lakestar, Orendt Studios, and prominent angels - we are building the full software stack and Operating System for enterprise fashion brands, enabling brands to create, scale, and connect with their customers like never before.