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.