Data Science / ML Engineer
Core
Design and implement machine learning solutions with an event-driven approach, focusing on deep learning models and ML-Ops pipelines to solve complex problems for clients.
Role type
Machine Learning Engineer (Deep Learning & ML-Ops)
Builds
Production ML models, event-driven data architectures, and scalable ML solutions
Domain
Technology consulting, data intelligence, and digital transformation
Deliverable
production ML models
Required skills
Python, TensorFlow, PyTorch, ML-Ops (MLflow, KubeFlow), cloud platforms (Azure Databricks, Microsoft Fabric, AWS SageMaker), data modeling, large dataset handling
Preferred skills
Event-driven architecture, Kafka, Pub/Sub, state-of-the-art deep learning techniques
Technologies
Kafka, Pub/Sub, TensorFlow, PyTorch, MLflow, KubeFlow, Azure Databricks, Microsoft Fabric, AWS SageMaker
Responsibilities
Design and set up machine learning solutions with an event-driven approach; develop and implement deep learning models; establish and optimize ML-Ops pipelines for CI/CD, deployment, and monitoring; handle data preprocessing and transformation; build scalable ML solutions on modern data platforms
Seniority
Mid-level, hands-on IC