Develop ML models for automated root cause analysis of validation and test failures
Build predictive analytics for failure forecasting and risk identification
Design pattern‑recognition algorithms to identify failure signatures in historical data
Create validation intelligence dashboards that support data‑driven engineering decisions
Implement AI‑driven test selection and prioritization based on code change risk
Develop systems to identify test coverage gaps and redundancies
Build optimization engines that reduce test runtime while preserving confidence
Develop AI‑powered test generation from requirements and code changes
Build adaptive and self‑healing test execution systems using historical performance data
Integrate Large Language Models (LLMs) into validation workflows to improve efficiency
Partner with firmware, hardware, and system architecture teams to define validation needs
Translate validation domain expertise into ML features with validation engineers
Collaborate with platform teams to deploy and scale AI/ML solutions
Drive AI adoption across validation teams through tooling, guidance, and enablement

8+ years of industry experience with a Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Electrical Engineering, or related technical field
7+ years of software development experience, primarily using Python
Strong foundation in machine learning fundamentals, including model evaluation and feature engineering
Hands‑on experience with ML frameworks such as scikit‑learn, TensorFlow, or PyTorch
Experience deploying and maintaining ML models in production environments

Principal AI/ML Engineer, Validation Strategy – Test Architecture

Key skills