Machine Learning Architect

Tasks

• Architect scalable data pipelines for AI ML workloads
• Define ML platform architecture
• Define reference architectures and best practices
• Design end to end ML workflows with Databricks
• Design secure and compliant AI architectures
• Drive cost optimization and scalability for ML platforms
• Establish MLOps best practices
• Evaluate and integrate AI capabilities
• Guide data scientists and ML engineers
• Optimize data models and feature stores
• Translate business needs into technical solutions

Perks/Benefits

• Child care vouchers
• Choose laptop and peripherals
• Flexible working hours
• Health insurance
• Open holidays
• Partnerships with local businesses
• Profit distribution
• Remote work options
• Snacks in office
• Training and conferences
• Unlimited hotspot usage

Skills/Tech-stack

AI Agents | AWS | Apache Airflow | Apache Spark | Automated retraining | Azure | CI/CD | Cloud platform | Databricks | Delta Lake | Distributed machine learning | Drift monitoring | ELT | ETL | Engineering Pipelines | Feature Engineering | Feature Engineering Pipelines | Feature Store | Google Cloud | Google Cloud Platform | Lakehouse | Language Models | Large Language Models | Lifecycle Management | MLOps | Machine Learning | Machine Learning Lifecycle Management | Machine learning lifecycle | Model Deployment | Model Drift | Model Governance | Model drift monitoring | Model versioning | Observability | Python | RAG | Retrieval-Augmented Generation | SQL | Unity Catalog | Vector Databases