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Mid-level ML role with popular title and broad production requirements increases applicant competition.
Requires manufacturing domain expertise and CAD/3D data experience, reducing cross-industry transferability.
Explicit 3–5 years plus production ML, frameworks, and MLOps requirements create strict shortlisting filters.
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Build, own, and deploy complex ML/DL subsystems end-to-end using manufacturing domain data types including geometries, metadata, and unstructured text.
Develop and maintain NLP and document understanding pipelines for technical drawings on AWS Bedrock-based AI platform.
Drive experimentation to solve complex ML problems independently and collaborate with cross-functional teams, while mentoring junior peers and contributing to technical design discussions.
3-5 years experience designing, training, and deploying complex ML/DL models in production using frameworks like PyTorch, TensorFlow, scikit-learn, XGBoost.
Bachelor's degree in computer science, simulation science, or equivalent with strong mathematical ML foundations.
Proficient in Python engineering including modular, testable code, data pipelines, CI/CD, and containerization (Docker).
Experience with ML experimentation tools (e.g., W&B, MLflow), data transformation tools (Pandas, SQL), and ability to deliver production-ready ML solutions independently.
Experienced ML engineer comfortable delivering autonomous decision-making ML models for critical business problems involving heterogeneous manufacturing data.
Able to independently manage ML subsystems from problem framing to live production deployment with a strong ownership mindset.
Capable of mentoring junior engineers while contributing technically in cross-functional teams, adapting state-of-the-art ML approaches to industrial manufacturing challenges.