Lead Engineer SQA in AI/ML

Silicon Labs

8 - 13 years

11 - 16 Lacs

hyderabad

Posted:7 hours ago| Platform:

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wireless computer science sqa artificial intelligence machine learning silicon natural language processing firmware middleware software quality assurance

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Work from Office

Job Type

Full Time

Job Description

Develop and execute test strategies for machine learning, deep learning, and Tiny LLM models running on IoT edge devices.
Validate model accuracy, robustness, and scalability under real-world IoT data conditions.
Design automated frameworks to test data pipelines, feature extraction, inference performance, and edge/cloud integration.
Ensure seamless integration of ML/DL modules into the IoT platform software stack (firmware, middleware, connectivity, and cloud APIs).
Collaborate with ML development teams to ensure models meet production-grade quality standards.
Work with DevOps engineers to integrate ML model validation into CI/CD workflows.
Drive design and deep-dive discussions with development teams to improve test coverage and model reliability.
Guide and mentor junior team members, fostering technical growth and best practices within the team.

Requirements :

Bachelor s degree in Electrical Engineering or Computer Science (or equivalent combination of education and experience.
8+ Years of relevant industry experience
Strong experience in software quality assurance, particularly in testing ML/DL models and systems.
Hands-on knowledge of machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
Familiarity with DevOps tools like Docker, Kubernetes, Jenkins, GitLab CI, etc., for automated builds, testing, and deployment.
Experience with ML model optimization for edge devices (e.g., TensorRT, OpenVINO, Edge TPU).
Understanding of machine learning, deep learning, and natural language processing.
Exposure to MLOps practices such as model versioning and lifecycle management.
Proven ability to lead technical discussions and mentor junior engineers.
Strong collaboration skills to work effectively across global teams.

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