AI Software Development Engineer in Test

placeBrasília calendar_month 
AI Software Development Engineer in Test - We are seeking a highly skilled AI Engineer to join our Software Quality Engineering (SQE) team.
The ideal candidate will have a dual focus: contributing to AI/ML solution development while performing responsibilities typical of a Software Development Engineer in Test (SDET).
This role involves building and deploying AI-powered solutions for enhancing software testing, automation frameworks, and processes.

Responsibilities: AI Engineering: 1.

AI Solution Development: 1.

Develop AI-powered solutions to improve software quality assurance processes.
Leverage frameworks like Retrieval-Augmented Generation (RAG) and other state-of-the-art AI/ML frameworks to design innovative testing and debugging tools.
Create models for triaging and debugging test case failures using structured and unstructured data, such as log files.
Framework Optimization: 2.
Enhance testing processes with AI to automate root-cause analysis, bug detection, and recommendation systems.
Collaborate with cross-functional teams to integrate AI solutions into existing software testing pipelines.
MLOps Development: 3.
Design, implement, and maintain robust MLOps pipelines for continuous integration and deployment of AI/ML models.

Automate workflows for model training, evaluation, and deployment.

SDET Responsibilities: 1.

Test Case Creation & Maintenance: 1.

Write, maintain, and optimize manual and automated test cases for functional, regression, and performance testing.
Collaborate with development teams to define test strategies and acceptance criteria for new features.
Automation Framework Development: 2.
Build and enhance automation frameworks for Android, Web, and backend systems using tools like Appium, Selenium, or equivalent.
Integrate automation pre-checks, post-checks, and reporting systems to ensure test case reliability.
Code Review & Collaboration: 3.
Review and provide feedback on code quality and design for testability.
Work closely with software engineers and other SDETs to drive testing best practices.
Test Data Management: 4.

Work with engineering teams to ensure proper test data management, including synthetic data generation for testing AI-based solutions.

Mandatory Skills Description: AI/ML Expertise: - Strong knowledge of machine learning frameworks (e.
g.
, TensorFlow, PyTorch, Scikit-learn).
  • Experience with MLOps tools (e.
g.
, MLflow, Kubeflow, AWS Bedrock, SageMaker).
  • Proficiency in RAG frameworks and understanding of knowledge-based systems.
  • Familiarity with LangChain and Hugging Face Transformers.
Experience working with Streamlit.
  • Strong command on all AWS services and prompt engineering techniques and Dspy.
SDET Expertise: - Proficiency in test automation tools (e.
g.
, Appium, Selenium, JUnit, TestNG).
  • Strong programming skills in languages like Python, Java.
  • Experience with CI/CD tools like Jenkins, GitLab CI, or equivalent.
General Skills: - Familiarity with cloud platforms (e.
g.
, AWS, GCP, Azure).
  • Knowledge of SQL and database management for test data validation.
  • Strong debugging and troubleshooting skills in software systems.
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