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Senior Software Engineer - Automation & Tooling

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Onsite

Senior Software Engineer - Automation & Tooling

iScale Solutions

Senior Software Engineer - Automation & Tooling

Skills

PythonJavaTesting & QAMachine LearningAIJavascript

22 days ago

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Important information


Contract type:

Permanent

Work mode:

Onsite

Published on:

5 October 2026

What they need


What You'll Do

  1. Build & Innovate – Design, develop, and maintain production-grade automation frameworks and tools that raise engineering efficiency and product quality.
  2. Lead by Example – Provide architectural direction, set coding standards, and remain hands-on in code contributions.
  3. Collaborate & Influence – Partner with cross-functional teams to solve complex quality challenges and drive org-wide improvements.
  4. Transform QA – Identify process gaps, integrate new technologies, and lead the adoption of modern automation practices.
  5. Mentor & Grow – Coach junior engineers, run knowledge-sharing sessions, and elevate QA capabilities across teams.
  6. Measure Impact – Use KPIs and data-driven insights to track progress and continuously improve quality strategies.
  7. Shape the Future – Align automation initiatives with customer experience, ensuring testing directly enhances product usability.

What You Bring:

  • 8+ years in Quality Engineering or Software Development with strong test automation expertise.
  • Minimum 7-8 years of software engineering experience is required.
  • Proven software engineering skills, with Python expertise (plus bonus points for Java, JavaScript, or others).
  • Proficiency in Java, Python, and JavaScript programming languages is required.
  • Experience architecting and scaling automation frameworks, integrated into CI/CD and modern DevOps.
  • Minimum 5+ years of automation testing experience is required.
  • A track record of both strategic influence and hands-on technical impact .
  • Strong leadership in mentoring, coaching, and guiding QA practices across teams.

Bonus Skills (AI/ML):

  • Experience building AI-powered workflows (e.g., RAG, LangChain, MCPs).
  • Hands-on with modern ML/AI libraries and MLOps practices.
  • Applying AI in QA contexts—like intelligent test generation or anomaly detection.
  • Awareness of ethical AI practices (bias, explainability, governance).

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