Data Infrastructure & MLOps Engineer

Join Doodle as a Data Infrastructure & MLOps Engineer, where you will design, build, and operate the platforms that enable data engineering, analytics, and machine learning. You will own the reliability, scalability, and automation of Doodle's data and machine learning platforms, and work closely with product, engineering, data, and security teams. Your responsibilities will include developing data pipelines, establishing data platform standards, and improving data discoverability and usability. You will also build and maintain MLOps workflows, operate machine learning workloads in production, and manage cloud-based data and machine learning infrastructure.

• Design, build, and operate scalable data infrastructure for ingestion, transformation, storage, and serving.
• Build and maintain MLOps workflows covering experimentation, data and model versioning, training, evaluation, deployment, and rollback.
• Partner with data scientists and software engineers to turn prototypes into reliable, maintainable production services.
• Professional experience in data engineering, platform engineering, MLOps, DevOps, or a closely related role
• A security-conscious approach to data access, privacy, secrets management, and production operations
• Experience with data warehouses, data lakes, workflow orchestration, and batch or streaming processing
• Practical knowledge of machine learning lifecycle management, model deployment, monitoring, and reproducibility
• Experience with observability, incident management, system reliability, and performance optimisation
• Strong Python and SQL skills, with experience developing production-quality software and data pipelines
• Strong communication skills and the ability to explain technical decisions to both technical and non-technical stakeholders
• Hands-on experience with cloud infrastructure, containers, CI/CD, and infrastructure as code
• Experience with tools such as Kubernetes, Terraform, Airflow, dbt, Spark, Kafka, MLflow, or similar technologies
• Experience operating machine learning systems in a B2B SaaS or high-growth technology environment
• Knowledge of feature stores, vector databases, LLM applications, retrieval-augmented generation, or agentic AI systems
• Experience implementing data quality frameworks, lineage, governance, and privacy controls
• Experience supporting ISO 27001, SOC 2, GDPR, or other security and compliance programmes
• Interest in building simple, scalable platforms that reduce operational complexity for other teams