At Aristocrat, we are advancing gaming technology with modern, powerful solutions. Our mission is to bring happiness to life through play. We value collaboration, creativity, and a strong passion for excellence. These roles primarily support the Machine Learning area of our Product Madness business line — a tech company specialised in social casino mobile games. We are looking for an MLOps Engineer to join our MLOps team and build and own the platform that takes our machine learning and AI systems reliably to production. You will focus on serving models as performant, real-time services, on the CI/CD and infrastructure around them, and on the tooling that lets our ML Engineers and Data Scientists ship, track and monitor models with confidence. You will make a real difference in our products and players' experiences.

What You'll Do

- Deploy machine learning models as reliable, real-time APIs, with a strong focus on latency, throughput, caching, and scaling.
- Design and own the serving and deployment infrastructure: containers, orchestration (Kubernetes), and serving frameworks (e.G., KServe, Triton, BentoML) where appropriate.
- Build and maintain CI/CD pipelines for models, and orchestrate training and retraining workflows (e.G., Airflow).
- Own the platform side of experiment tracking and model registry: versioning, lineage, promotion, and governance, so model releases are controlled and reproducible.
- Build the monitoring and observability infrastructure that ML Engineers use to track model and system health (metrics, logging, tracing, alerting).




- Ensure reproducibility across environments and pipelines, and harden systems for reliability (retries, timeouts, graceful degradation, rollback).
- Partner with ML Engineers and Data Scientists to smooth the path from prototype to production, and with engineering on deeper system-design questions.

What We're Looking For

- 4+ years in ML infrastructure, ML platform, or MLOps roles, ideally having worked inside a dedicated MLOps or platform team.
- Strong software and systems engineering foundations, with a clear background in engineering or Computer Science.
- Proven experience serving models as real-time, low-latency services, with a solid grasp of caching, concurrency, and application performance (p95/p99).
- Hands-on experience with Docker, Kubernetes, CI/CD, and cloud platforms (e.G., GCP, AWS, Azure).
- Experience with workflow orchestration (Airflow, Prefect, Kubeflow, or similar) and with experiment tracking / model registry tooling (MLflow, Weights & Biases).
- Solid Python skills and comfort operating production services and their observability stack (e.G., Prometheus, Grafana).

Nice to Have

- Experience with Snowflake or similar cloud data warehouses.
- Experience supporting GenAI/LLM applications in production (serving, evaluation infrastructure, vector databases).
- Exposure to designing AIOps-oriented infrastructure.
- Interest in the ML/modeling side, enough to collaborate closely with ML Engineers (you do not need to be a researcher).

📌 Mlops Engineer (Barcelona)
🏢 Product Madness Espana
📍 Barcelona