Apply engineering guidelines: Write clean, maintainable Python code, implement automated tests, participate in code reviews, and maintain technical documentation.
Work with complex data: Retrieve, integrate, validate, and prepare datasets from multiple sources for model training, inference, and evaluation.
Refine existing solutions: Examine technical problems, improve model performance, and support advancements in reliability and efficiency.
Collaborate across teams: Work with Data Scientists, engineers, and product collaborators to understand requirements and deliver practical ML/AI solutions.
Develop technical expertise: Continuously acquire new ML/AI technologies, share knowledge, and contribute to engineering discussions and team practices.
What We're Looking For
Education: BSc, MSc, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related technical field, or equivalent experience in practice.
Experience: 2+ years of professional experience in software engineering, machine learning engineering, or AI development, with practical experience developing ML models or AI-powered applications.
Software engineering: Strong Python programming skills, including software build, testing, debugging, version control, and collaborative development practices.
Machine learning: Hands-on experience developing, training, and evaluating supervised learning models using frameworks such as scikit-learn, PyTorch, TensorFlow, or similar.
Generative AI: Experience building applications using LLMs, including timely query formulation, structured outputs, RAG, and tool calling.
ML/AI evaluation: Understanding of model evaluation methodologies, performance metrics, and approaches to validating AI outputs.
ML specialization: Practical experience in at least one area such as recommender systems, time-series modeling, unsupervised learning, or reinforcement learning.
Data processing: Strong SQL skills and experience retrieving, transforming, and validating data from analytical databases and APIs.
Production ML: Familiarity with model deployment, inference services, monitoring, and ML lifecycle management.
Cloud & infrastructure: Working knowledge of cloud platforms (e.g., GCP, AWS, Azure), containers, CI/CD pipelines, and ML development environments.
Nice to have
Experience in mobile gaming, digital entertainment, or large-scale consumer applications.
Experience developing recommendation or personalization systems.
Familiarity with agentic workflows, AI agents, or advanced LLM applications.
Experience with model serving, distributed computing, or performance optimization.
Experience working with managed ML platforms such as Vertex AI.
Familiarity with Bayesian modeling, causal inference, or simulation techniques.
Important Information for Applicants
CV Language: Please submit your CV in English.
Hybrid Working Policy: This role follows a hybrid working model, requiring attendance at our Barcelona office 3 days per week.
This job description may have been reviewed and enhanced using AI-assisted tools to improve clarity, consistency, and inclusivity. All final content, role requirements and hiring decisions remain subject to human review and approval by Aristocrat.
Compensation Philosophy
We offer a comprehensive pay and benefits package designed to stay competitive in the market, support your wellbeing, and recognise your contribution to our success. Our approach is underpinned by a pay-for-performance belief in rewarding individual impact. Your specific compensation package will be determined by factors such as your skills, experience, qualifications, and location.
Depending on your role and location, you may be eligible for annual bonuses and incentives, health and wellbeing benefits, paid time off, retirement plans, insurance coverage, and other local or statutory benefits.
Specific details about compensation and benefits for this position will be discussed during the recruitment process.
