Junior ML DataOps Engineer (AI)

Location: London, United Kingdom

Company

hirify.global is a European betting and gaming operator with more than 5,000 employees across around fifteen regulated markets, developing lottery and sports-betting products and data-driven player experiences.

What you will do

• Build, operate, and improve tools, infrastructure, and workflows supporting Data Scientists, Data Engineers, and Machine Learning Engineers.
• Automate processes and improve data quality, testing, monitoring, and deployment reliability.
• Contribute to Data Lakehouse capabilities and tooling for efficient, safe experimentation and production delivery.
• Explore and adopt technologies including Generative AI to improve data-product development and operations.
• Work with experienced engineers across software engineering, data engineering, cloud infrastructure, and AI operations.

Requirements

• Good programming skills in Python or Java and willingness to develop further.
• Understanding of software engineering fundamentals, including version control, code reviews, testing, and maintainable code.
• Interest in DataOps, DevOps, MLOps, or ML Engineering and reliable production systems.
• Familiarity with CI/CD pipelines and automated deployment, or willingness to learn.
• Understanding of data pipelines, data processing, data quality, and reliability.
• Interest in AI and machine learning, including Generative AI and the operational challenges of productionizing AI solutions.
• Familiarity with cloud computing, ideally AWS, or experience with cloud-based projects.
• Proactive problem-solving skills, including investigating issues, learning from feedback, and developing practical solutions.
• Good communication and interpersonal skills, collaboration across disciplines, growth mindset, and ability to work with uncertainty and changing priorities.

Nice to have

• Experience with Python-based data processing or automation.
• Familiarity with AI/ML frameworks, APIs, or Generative AI tools.
• Exposure to Spark, Kafka, Docker, containers, Kubernetes, Terraform, CloudFormation, AWS services, or cloud-native application development.
• Understanding of automated testing, data quality checks, monitoring, logging, or alerting.
• Experience with Git-based workflows and tools such as GitLab.
• Interest in data platforms, Data Lakehouse architectures, or enterprise data concepts.
• Personal projects, academic work, or contributions demonstrating practical experience in software engineering, data, or AI.

Culture & Benefits

• Permanent contract with a collaborative, accountable, performance-driven engineering culture.
• Learning and development opportunities and continuous exposure to new technologies.
• Private medical insurance, pension, Employee Assistance Programme, and long-service awards.
• Social events, activities, and inclusion networks.
• Inclusive hiring based on skills, experience, and potential, regardless of personal characteristics.