Architect AI Engineer / AI Evangelist, Healthcare Business Unit (EMEA)
Summary
Design and evangelize AI/ML solutions for healthcare clients, building predictive models, NLP workflows, and MLOps pipelines while ensuring compliance and scalability.
Requirements
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or related field.
7+ years of experience in data science or machine learning roles, ideally with exposure to healthcare projects.
Strong knowledge of ML frameworks such as scikit-learn, TensorFlow, PyTorch, XGBoost, or LightGBM.
Proficiency in Python for data science and related libraries (NumPy, pandas, matplotlib, seaborn, etc.).
Experience working with large datasets and data processing frameworks (e.g., Spark, Dask, SQL).
Understanding of MLOps concepts and tools (e.g., MLflow, Kubeflow, Vertex AI, Azure ML).
Familiarity with cloud environments (Azure, AWS, or GCP) for training and deploying models.
Experience with model interpretability, fairness, and explainability techniques.
Strong communication and visualization skills for storytelling with data.
English proficiency at Upper-Intermediate level or higher.
Preferred Qualifications (Nice to Have)
Experience working with medical data (EHR, imaging, wearables, clinical trials, etc.).
Familiarity with healthcare regulations related to data and AI (e.g., HIPAA, GDPR, FDA AI/ML guidelines).
Knowledge of FHIR, HL7, or other healthcare interoperability standards.
Practical experience with deep learning models (e.g., CNNs for imaging, transformers for NLP).
Involvement in presales, proposal writing, or technical advisory work.
Job responsibilities
Lead the design and development of AI/ML solutions across HealthTech and MedTech projects.
Participate in technical presales by analyzing business cases and identifying opportunities for AI/ML application.
Build and validate predictive models, classification systems, NLP workflows, and optimization algorithms.
Collaborate with software engineers, cloud architects, and QA to integrate models into scalable production systems.
Define and guide data acquisition, preprocessing, labeling, and augmentation strategies.
Contribute to the development of GlobalLogic’s healthcare-focused AI accelerators and reusable components.
Present technical solutions to clients, both business and technical audiences.
Support model monitoring, drift detection, and retraining pipelines in deployed systems.
Ensure adherence to privacy, security, and compliance standards for data and AI usage.
Author clear documentation and contribute to knowledge sharing within the Architects Team.