Key skills
AWSCloudETLPythonTerraformAIMLGenerative AIGenAIAgenticMLOpsData EngineeringSageMakerSales
About this role
Role Overview
- Take technical ownership for AWS cloud-based Data, ML & AI solutions
- Build and operate end-to-end ML workflows
- Design and implement modern data architectures and data pipelines
- Deploy and maintain ML models using AWS SageMaker
- Implement Generative and Agentic AI use cases using the AWS ecosystem
- Guide customers on how to adopt GenAI pragmatically and responsibly
- Act as a trusted technical advisor for customers
- Support pre-sales activities and technical workshops
- Translate complex technical concepts into clear business value
- Mentor junior colleagues
- Contribute to internal standards, best practices and trainings
Requirements
- 2-3+ years of experience in cloud, data or ML engineering
- Proven hands-on expertise with AWS
- Solid experience in data engineering and ETL architectures
- Practical MLOps experience (model deployment, monitoring, lifecycle)
- Very good Python skills
- Proven experience with IaC (Terraform or comparable)
- Fluent in English
- Consulting or customer-facing project experience
- Experience with Generative AI projects
Tech Stack
- AWS
- Cloud
- ETL
- Python
- Terraform
Benefits
- 4-days-week (36h) with full focus on impact
- Fast & transparent hiring process (max. 3 interview rounds, 1 interview in person, typically max. 4 weeks from application to offer)
- Structured onboarding
- Fully funded AWS certifications
- Remote-friendly setup with limited travel (max. 25%)
- Regular team & afterwork events in Vienna, Hamburg, Belgrade and virtually