Required Experience

Minimum 3-5 years of experience in AI/ML, Data Science, or Applied AI Engineering.

Strong proficiency in Python, machine learning, and data analysis.

Hands-on experience with deep learning, transformer-based architectures, and modern AI frameworks (e.g., PyTorch, TensorFlow).

Experience in one or more of the following domains: computer vision, generative AI, foundation models, diffusion models, predictive modelling.

Developing, validating, and deploying AI/ML solutions in production or enterprise environments.

Working with cross-functional teams comprising of domain experts, software engineers, product owners, and business stakeholders.

Experience in medical imaging, healthcare AI, supply chain analytics, or other applied AI domains.

Required

Skills

- AI Model Development

Computer vision

Generative AI

Predictive modelling

Optimisation

Model fine-tuning 2.

Clinical

Imaging AI

Medical imaging AI

MRI image understanding

Image quality assessment

Model Explainability 3. Data Quality & Validation

Data cleansing

Annotation review

Dataset readiness

Statistical validation

Failure analysis 4. Deployment & MLOps

Experiment tracking

Model versioning

Deployment support

Monitoring

Documentation 5. Collaboration & Delivery

Cross-functional collaboration

Requirement clarification

Stakeholder interaction

Technical documentation – compliant with defined procedures and templates

Productization support Job Responsibilities:

Collaborate with Philips teams on applied AI use cases across clinical imaging and supply chain domains.

Contribute to the development, evaluation, and refinement of AI/ML models,



including computer vision, generative AI, predictive modelling, and optimisation-based approaches, depending on the use case.

Work with clinical, engineering, business, and IT stakeholders to translate problem statements into clear AI requirements, success criteria, data needs, and measurable outcomes.

Support data preparation activities such as data cleansing, validation, quality checks, annotation review, dataset structuring, and readiness assessment to ensure reliable model development and evaluation.

Develop, train, retrain, and fine-tune AI models using appropriate techniques, including statistical analysis, hypothesis testing, performance evaluation, explainability, and failure analysis.

For clinical imaging use cases, contribute to image-based AI model development and validation, including MRI image generation, artefact detection, image quality assessment, and model explainability.

For supply chain use cases, contribute to AI solutions involving forecasting, decision intelligence, workflow automation, and data-driven process improvement.

Integrate AI models into deployable workflows or product environments in collaboration with software, platform, and DevOps teams, ensuring performance, scalability, reliability, and maintainability.

Contribute to model lifecycle activities, including experiment tracking, model versioning, monitoring, documentation, validation evidence, and continuous improvement.

Participate actively in design discussions, code reviews, testing, quality assurance, and knowledge-sharing activities, while working closely with Philips team members in a cooperative delivery model.

📌 Data Scientist (Bengaluru)
🏢 Cyient
📍 Bengaluru