Software Development Engineer in Test

Software Development Engineer in Test x2

• Full time : 70k – 80k max / 2nd option : contract : 450 – 500 GBP inside
  
  
• 5 days onsite mandatory
  
  We are looking for the Software engineer who have experience in working in Test Engineering role and hands-on experience in Python, AWS and testing tools like pytest, playwright.
  
  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
  
  • 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