Software Development Engineer in Test

4 weeks ago

City of London Greater London, United Kingdom Insight International (UK) Ltd Full time

Job Title: SDET (Software Development Engineer in Test) Location: London, UK – 5 days onsite mandatory. Contract/Permanent (Both options are open) 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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