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

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AWSdata engineeringETL architecturesMLOpsPythonInfrastructure as CodeTerraformML workflowsML model deploymentGenerative AI

Soft Skills

technical ownershipmentoringcustomer-facingcommunicationadvisingtranslating technical conceptspre-sales supportworkshop facilitationcollaborationproblem-solving