{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Senior Software Development Engineer Test”, “description”: “
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.
Company: Alphayotta
Location: London
Job Description:
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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Posted: May 20th, 2026