SDET (On-Site)

SDET (Software Development Engineer in Test) profile, in short, 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

• 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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