ML Engineer

Aristocrat

ML Engineer enhancing AI capabilities and machine learning systems for gaming technology at Aristocrat. Focused on developing production-ready models and collaborating with multidisciplinary teams in Barcelona.

Posted 7/18/2026full-timeBarcelona • 🇪🇸 SpainMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying machine learning models and refining code quality using Python and SQL, while effectively communicating results to both technical and business stakeholders. Proficient in building user-friendly interfaces for ML applications and collaborating across teams to enhance operational efficiency.

Highest-signal resume keywords

Machine Learning ApplicationPython ProgrammingSQL ExpertiseCloud Platform ExperienceGenerative AI Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonSQLData TransformationFeature EngineeringModeling PipelinesStreamlitGenerative AIAPI IntegrationRAG Systems

Soft Skills

CuriosityInitiativeDesire to Learn

Tools & Technologies

GCPAWSAzureDockerAirflow

Industry Keywords

AI ApplicationsModel MonitoringDecision SupportAutomationAgentic AI

Tech Stack

Tools & technologies

AirflowAWSAzureCloudDockerGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Deploy machine learning models that support game features, player experience, and operational efficiency.
Improve the code quality of others' work by refining SQL and Python scripts for data transformation, feature engineering, modelling pipelines, and AI app, thereby ensuring production readiness.
Explore and contribute to early-stage projects involving LLMs, RAG systems, and agentic AI to power new types of automation and decision support.
Develop or refine Streamlit-based tools (or alike) that present ML/AI capabilities to non-technical users via intuitive and interactive interfaces.
Help build and implement model monitoring systems to ensure long-term performance and business alignment.
Collaborate with teams across product, data, and engineering to identify where ML and AI can bring value.
Communicate results, limitations, and recommendations clearly across technical and business collaborators.

Requirements

What you’ll need

5+ years of experience applying machine learning to real-world problems, from data to deployment.
Strong Python skills and hands-on experience with ML Python libraries.
Solid SQL expertise and experience working with large data warehouses.
Comfort working with cloud platforms (e.g., GCP, AWS, Azure), Docker, and Airflow.
Ability to build simple user interfaces (e.g., Streamlit) to expose ML models or AI applications to wider teams.
Hands-on experience building applications with Generative AI (LLMs), including API integration.
Interest or early experience in RAG systems—whether through prototypes, side projects, or professional work.
Interest or familiarity with agentic AI concepts or frameworks (e.g., LangChain agents, CrewAI) is a plus, but not a must.
Curiosity, initiative, and a desire to learn and grow in a fast-evolving space.
Degree in a relevant technical field or equivalent experience.

Benefits

Comp & perks

Robust benefits package