Senior Machine Learning Engineer

LinkGroup is a global technology organisation developing data-driven products used by millions of customers worldwide.

• Strong hands-on experience in Machine Learning Engineering and Data Engineering, ideally 5+ years building and operating production ML systems.
• Experience taking machine learning models or data products from experimentation into production.
• Strong Python skills and experience with technologies such as Spark and Databricks.
• Experience with ML platforms and tooling such as MLflow.
• Practical understanding of production ML systems including pipelines, model serving, monitoring, and observability.
• Experience with cloud, platform, or backend technologies such as Kafka, Kubernetes, or Go is advantageous.
• Strong statistical literacy and ability to design and interpret experiments and model metrics.
• Experience with AI development tools such as Claude Code, Cursor, or GitHub Copilot.
• Strong problem-solving and troubleshooting skills.
• High level of ownership and pragmatic, delivery-focused mindset.
• Ability to work effectively in cross-functional and international environments.
• Good product sense connecting technical solutions with business and user outcomes.

• Build, maintain, and improve ML infrastructure, repositories, developer tooling, and engineering workflows.
• Design and operate data products and machine learning systems from experimentation through to production.
• Build robust data pipelines and integrations supporting machine learning and decisioning systems.
• Develop scalable AI-powered solutions for content generation, search, and asset retrieval.
• Build production-grade pipelines and model-serving layers focusing on reliability, latency, and observability.
• Collaborate closely with Data Scientists to turn experimental models into production services.
• Establish and improve ML engineering standards, CI/CD practices, and platform solutions, including Databricks and MLflow.
• Monitor production systems, data quality, model performance, drift, and latency.
• Diagnose issues under real-world load and implement reliable solutions.
• Take ownership of systems built and continuously improve based on data and user outcomes.
• Collaborate with engineers, data scientists, and product stakeholders across international teams.
• Use AI-assisted development tools daily.