Cloud ML & DevOps Engineer for AI Data Solutions

your Jared; Germany (OH); Hybrid; USD 104,000 - 150,000; Full time

Benefits offered by this job

Flexible work environment

Public transport ticket (Deutschlandt

Gym membership subsidy

Job summary

Machine Learning Reply is seeking DevOps and ML Engineers to support customer projects across industries, focusing on data processing and ML workloads. You will design approaches, deploy on cloud or on-prem, and implement MLOps solutions.

The role offers a hybrid work environment between client sites, Reply offices, and remote work, with opportunities to collaborate with enterprise architects, data scientists, and engineers to grow in cloud, data engineering, and AI domains.

Qualifications

• Bachelor’s / Master’s degree in computer science or related field.
• Experience with DevOps/MLOps on cloud platforms (AWS/Azure/GCP) and Databricks.
• Ability to communicate analytics to management.
• End-to-end data lifecycle: Cloud, Data Engineering, Analytics, Visualization, ML & MLOps.
• Fluent in English and German.

Responsibilities

• Design technical approaches for data-intensive ML applications.
• Implement solutions on cloud or on-prem infrastructures.
• Automate tasks using DevOps and MLOps practices.
• Ensure monitoring, failover and recovery aligned with regulatory requirements.
• Interact with customers and stakeholders to translate requirements into production-ready solutions.
• Collaborate with architects, analysts, data scientists, and data engineers.

Skills

DevOps

MLOps

Python

Java

Scala

SQL/NoSQL

Spark

Kafka

Airflow

Databricks

Cloud platforms (AWS/Azure/GCP)

Education

Bachelor's / Master’s in CS or related

Tools

Kubernetes

Job description

Machine Learning Reply is seeking DevOps and ML Engineers to support customer projects across industries, focusing on data processing and ML workloads. You will design approaches, deploy on cloud or on-prem, and implement MLOps solutions.

The role offers a hybrid work environment between client sites, Reply offices, and remote work, with opportunities to collaborate with enterprise architects, data scientists, and engineers to grow in cloud, data engineering, and AI domains.