Python Test Engineer

Cognizant; City Of London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A technology consultancy based in the City of London is seeking a Lead Software Engineer to enhance their AI/ML Data Platform. This role involves architecting, developing, and validating reliable, high-performance tools and services for ML data pipelines. The ideal candidate has significant experience in software development, is proficient in Python, and understands CI/CD systems. If you're passionate about quality engineering and making a tangible impact, this could be your opportunity.

Qualifications

• Extensive experience in software development with backend systems.
• Expert knowledge in Python and object-oriented programming.
• Hands-on knowledge of CI/CD systems and automation libraries.

Responsibilities

• Design and build high-performance tools for validating ML data pipelines.
• Develop platform-level test solutions and automation frameworks using Python.
• Collaborate with teams to embed quality engineering into platform components.

Skills

Python

Infrastructure as Code

CI/CD

Automation frameworks

AWS services

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Terraform

GitHub Actions

Jenkins

Job description

Job Description

we are building an enterprise-grade AI/ML Data Platform that enables scalable, secure, and responsible machine learning across the firm. We are seeking a Lead Software Engineer with a strong background in software development and platform engineering to help drive the Test Engineering Program—a strategic initiative focused on building robust, intelligent validation frameworks and infrastructure that power our ML and data products.

This is a software engineering role embedded in quality and reliability initiatives. You’ll architect and develop tools, services, and automation that elevate platform assurance across large-scale distributed systems. If you're passionate about platform quality, CI/CD excellence, infrastructure-as-code, and bringing engineering rigor to validation, this is your opportunity to make a major impact.

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.
• Extensive 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.
• Pytest/Playwright test framework.
• 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.