Key skills
AWSCloudETLPythonTerraformAIMLGenerative AIGenAIMLOpsData EngineeringSageMakerBedrockMentoringSales
About this role
Role Overview
- Take technical ownership of AWS cloud-based Data, ML and AI solutions
- Design scalable AWS architectures for data platforms, ML workloads and GenAI solutions
- Lead the implementation and operation of data pipelines, MLOps setups and GenAI deployments
- Ensure security, performance and reliability in production environments
- Build and operate end-to-end ML workflows
- Deploy and maintain ML models using AWS SageMaker
- Implement Generative AI use cases using AWS Bedrock
- Advise customers on pragmatic and responsible use of GenAI
- Act as a trusted technical advisor for clients
- Support pre-sales activities and run technical workshops
- Translate complex technical concepts into clear business value
- Mentor junior colleagues
- Contribute to internal standards, best practices and training materials
Requirements
- 4+ years of experience in cloud, data or ML engineering
- Strong hands-on expertise with AWS
- Solid experience in data engineering and ETL architectures
- Practical MLOps experience (model deployment, monitoring, lifecycle management)
- Very good Python skills
- Proven experience with Infrastructure as Code (Terraform or equivalent)
- Fluent in German and English
- Consulting or customer-facing project experience
Tech Stack
- AWS
- Cloud
- ETL
- Python
- Terraform
Benefits
- 4-day work week (36 hours) focused on impact
- Fast & transparent hiring process (max. 3 interview rounds, typically within 4 weeks from application to offer)
- Personal mentoring and structured onboarding
- Fully funded AWS certification training
- Remote-friendly setup with limited travel (maximum 25%)
- Regular team and after-work events in Vienna, Hamburg, Belgrade and virtually