Software Engineer in Test

5 days ago

London Area, United Kingdom Q1 Technologies, Inc. Full time £90,000 - £120,000 per year

Key Responsibilities


• Design and build high-performance tools and services to validate the reliability, performance, and correctness of ML data pipelines and AI infrastructure.


• Develop platform-level test solutions and automation frameworks using Python, Terraform, and modern cloud-native practices.


• Contribute to the platform's CI/CD pipeline by integrating automated testing, resilience checks, and observability hooks at every stage.


• Lead initiatives that drive testability, platform resilience, and validation as code across all layers of the ML platform stack.


• Collaborate with engineering, MLOps, and infrastructure teams to embed quality engineering deeply into platform components.


• Build reusable components that support scalability, modularity, and self-service quality tooling.


• Mentor junior engineers and influence technical standards across the Test Engineering Program.

Required Qualifications


• Bachelor's or master's degree in computer science, Engineering, or a related technical field.


• 8+ years of hands-on software development experience, including large-scale backend systems or platform engineering.


• Expert in Python with a strong understanding of object-oriented programming, testing frameworks, and automation libraries.


• Experience building or validating platform infrastructure, with hands-on knowledge of CI/CD systems, GitHub Actions, Jenkins, or similar tools.


• Solid experience with AWS services (Lambda, S3, ECS/EKS, Step Functions, CloudWatch).


• Proficient in Infrastructure as Code using Terraform to manage and provision cloud infrastructure.


• Strong understanding of software engineering best practices: code quality, reliability, performance optimization, and observability.

Preferred Qualifications


• Exposure to machine learning workflows, model lifecycle management, or data engineering platforms.


• Experience with distributed systems, event-driven architectures (e.g., Kafka), and big data platforms (e.g., Spark, Databricks).


• Familiarity with banking or financial domain use cases, including data governance and compliance-focused development.


• Knowledge of platform security, monitoring, and resilient architecture patterns.

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