Data Infrastructure & MLOps Engineer

• As a Data Infrastructure & MLOps Engineer, you will design, build, and operate the platforms that enable data engineering, analytics, and machine learning across Doodle. Working closely with product, engineering, data, and security teams, you will make data easier to access, models easier to deploy, and systems easier to monitor and maintain
• As a Data Infrastructure & MLOps Engineer, you will own the reliability, scalability, and automation of Doodle’s data and machine learning platforms
• Design, build, and operate scalable data infrastructure for ingestion, transformation, storage, and serving
• Develop reliable batch and streaming data pipelines that support product analytics, business intelligence, and machine learning use cases
• Establish data platform standards for performance, availability, observability, documentation, and cost management
• Improve data discoverability and usability through data cataloguing, lineage, ownership, and quality processes.

Machine Learning Operations

• Build and maintain MLOps workflows covering experimentation, data and model versioning, training, evaluation, deployment, and rollback
• Operate machine learning workloads in production, including model serving, feature pipelines, scheduled retraining, and inference infrastructure
• Partner with data scientists and software engineers to turn prototypes into reliable, maintainable production services
• Introduce repeatable approaches for model validation, monitoring, drift detection, performance measurement, and incident response
• Manage cloud-based data and machine learning infrastructure using infrastructure as code and automated deployment practices
• Build secure, reproducible environments for development, testing, and production
• Improve platform efficiency through automation, capacity planning, resource optimisation, and sensible cost controls
• Contribute to platform architecture decisions and help evolve Doodle’s technical foundations as the business grows
• Define and maintain service level objectives, operational runbooks, alerts, dashboards, and on-call processes for critical data and ML systems
• Protect sensitive data through appropriate access controls, encryption, secrets management, retention policies, and secure development practices
• Support compliance, privacy, and responsible AI requirements by making data and model operations traceable, auditable, and well documented
• Investigate incidents, lead root cause analysis, and implement preventative improvements across the platform
• Work with product, engineering, analytics, data science, security, and operations teams to understand requirements and deliver practical platform solutions
• Create clear documentation, reusable tooling, and self-service workflows that enable teams to work independently
• Contribute to engineering standards, technical planning, code reviews, and knowledge sharing

• 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