Philips
From Philips's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Collaborates extensively on AI DevOps activities, leveraging multiple tool sets at the solution level to streamline development, testing, inference, deployment, and monitoring processes with moderate complexity.
Participates in discussions related to technical requirements, designs, and quality standards, providing inputs to ensure the robustness, scalability, and maintainability of AI solutions, working under direct supervision.
Integrates deployable versions of machine learning models developed by data scientists into end products, ensuring compatibility, performance, and reliability of all system components.
Handles data processing activities, including data cleansing, validation, and verification, to ensure the integrity and quality of data used for analysis and model training, with a focus on continuous improvement.
Trains and retrains AI systems as necessary to adapt to changing data distributions, business requirements, and performance objectives, optimizing model performance through iterative refinement.
Performs statistical analysis and fine-tuning of AI models using test results, hypothesis testing, and validation techniques to validate model assumptions, identify areas for improvement, and optimize model parameters.
Contributes to activities within the on-site engineer team, providing the whole deployment plan and pipelines.
Participates in the AI development process, working in pairing mode with equal team members, and actively contributing to requirements gathering, design discussions, code reviews, and quality assurance activities.
Interacts with business, market, and IT stakeholders to formulate clear and actionable requirements for AI solutions, ensuring alignment with business goals, user needs, and technical capabilities.
Ensures the quality of data and AI solutions developed, conducting thorough testing, validation, and verification activities to identify and address defects, errors, and performance issues before deployment.
Master or PHD candidate in Biomedical Engineer, Computer Science, Information Management, Data Science, Artificial Intelligence, Applied Mathematics, Statistics or equivalent.
Experience with Data processing, Data Analytics, AI Modeling or equivalent.
Experience in using programming languages such as Python, R, JAVA, C/C++
Artificial Intelligence Board of America (ARTiBA) certified
Troubleshooting
Data Analytics
Statistical Methods
Data Harmonization & Processing
Artificial Intelligence (AI)
Scripting & Automation
AI Algorithm Development
DevOps
Data Governance
LLM deployment
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