Synthetic Data Methodology : Design physics-constrained generative model approaches (diffusion models, VAEs) for synthetic data generation. Deliver validated methodology and training recipes for pipeline operationalization.
IP & Domain InterMface: Demonstrate strong research output through preprints, or patent disclosures. Interface with storage domain expert to validate physics constraints before deployment. Produce validated research prototypes with complete technical documentation to team-handoff standard. Participate in design reviews as the research methodology authority.
Master's or PhD in Artificial Intelligence, Machine Learning, Physics, Applied Mathematics, or related field. Strong AI/ML research focus and scientific computing background required.
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MLOps Platform Ownership: Own the full ML platform: MLflow, Docker, AWS EKS/Kubernetes, LLM Gateway (LangFuse/PortKey), CI/CD, observability, and model monitoring. Architect the platform; delegate maintenance to team once stable.
Technical Direction & Team Architecture: Lead design reviews and code reviews; provide technical direction to team; define data interface contracts with the DE; partner with domain scientists from problem definition through deployment; mentor junior team members.
Bachelor's or Master's degree in Artificial Intelligence, Machine Learning, Computer Science, or related field. AI major or strong AI research focus preferred. Equivalent depth demonstrated through open-source contributions, or significant GitHub portfolio will be considered.
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Conduct comprehensive quality inspections and analysis of incoming materials, including dimensional verification, material composition analysis, and surface quality assessment
Perform root cause analysis on quality issues and non-conformances, documenting findings and implementing corrective actions to prevent recurrence
Maintain detailed records and documentation of all quality testing, material certifications, and compliance data in accordance with ISO standards and regulatory requirements
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Providing technical support for computing scope by phone or physical touch
Repairing and replacing computer hardware as necessary
Candidate must possess at least a Diploma in Computer Science, Information Technology, Computer Technology, Computer/Telecommunication Engineering or equivalent
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Working experience in clean room environment will be an added advantage.
Familiar with facility equipment like Air Handling Units, Air-Conditioners, Chillers, Cooling Towers, Air Compressors, Air- Dryers, Vacuum System, Ring Blower, Scrubber and others mechanical system for a Manufacturing Facility.