Real-Time MLOps Engineer

Aristocrat Gaming; Barcelona; On-site; EUR 70,000 - 95,000; Full time

Job summary

Aristocrat Gaming in Barcelona is seeking an experienced MLOps Engineer to join our MLOps team and build the platform that takes our machine learning and AI systems reliably to production. You will focus on serving models as 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.

Qualifications

• 4+ years in ML infrastructure, ML platform, or MLOps roles, ideally in dedicated teams.
• Strong software and systems engineering foundations in CS or engineering.
• Proven experience serving models as real-time, low-latency services with caching and concurrency.
• Hands-on experience with Docker, Kubernetes, CI/CD, and cloud platforms (GCP, AWS, Azure).
• Experience with workflow orchestration and experiment tracking / model registry tooling (MLflow, Weights & Biases).
• Solid Python skills and comfort with production services and observability stacks (Prometheus, Grafana).

Responsibilities

• Deploy ML models as reliable, real-time APIs with focus on latency, throughput, caching, and scaling.
• Design and own serving infra: containers, orchestration (Kubernetes), and serving frameworks (KServe, Triton, BentoML).
• Build and maintain CI/CD pipelines for models and orchestrate training/retraining workflows (Airflow).
• Own platform side of experiment tracking and model registry: versioning, lineage, promotion, governance.
• Build monitoring and observability for ML Engineers: metrics, logging, tracing, alerting.

Skills

MLOps

Python

Docker

Kubernetes

CI/CD

Airflow

Model serving

Tools

MLflow

Weights & Biases

KServe

Triton

Job description

Aristocrat Gaming in Barcelona is seeking an experienced MLOps Engineer to join our MLOps team and build the platform that takes our machine learning and AI systems reliably to production. You will focus on serving models as 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.