Senior AI/ML Engineer for a data spaces platform

Project Description

We are looking for a Senior ML/AI Engineer who will work on developing federated learning and AI features with privacy-preserving techniques.

You will work on a platform that automates data ingestion, processing, and sharing with user-friendly, privacy-preserving, and scalable solutions for industrial manufacturing. 

The platform will incorporate scalable and dynamic tools for creating and managing data spaces, handling complex data workflows, and ensuring modularity and privacy compliance. 

Preliminary Project's Stack:

• Backend: Python, Flask/FastAPI, Go
• Frontend: ReactJS, Angular.
• AI/ML: Azure Machine Learning, Azure Databricks, TensorFlow Federated, PyTorch, and privacy-enhancing techniques.
• Cloud and DevOps: Kubernetes, Docker, Azure DevOps, CI/CD Data pipelines on Azure
• Data Engineering: Apache NiFi. Kafka Connect, Databricks - on Azure.
• Database: Cosmos DB, Postgres/Hyperscale or MySQL/Healwave

*stack may change during the hiring process of qualified specialists in their areas

Responsibilities:

Your primary responsibilities will include integrating contextualization and schema mapping models into real-time workflows, mentoring team members, contributing to system design, and managing AI/ML complexities.

Requirements:

• Experience with ML models, algorithms model optimization, and distributed training techniques (federated learning, e.g., FedAvg, secure aggregation). 
• Familiarity with Distributed systems, cloud-native design, and hybrid platforms
• Building custom ML pipelines on Azure ML
• TensorFlow, PyTorch, or scikit-learn 
• Familiar with Data processing 

Would be a plus:

• Container orchestration on AKS or Azure Arc