About the role

Role Overview

DevOps/ML Engineer (m/f/d) at Machine Learning Reply. You will support customer projects around machine learning and data processing, acting as a consultant responsible for delivering technical expertise and solutions.

Responsibilities

Design and implement technical approaches for data-intensive applications with a focus on machine learning and AI.
Take ownership of solutions on cloud (AWS, Azure, or GCP) and/or on-prem customer infrastructures.
Automate recurring work using DevOps and MLOps concepts to reduce customers’ time-to-delivery.
Provide monitoring, failover, and recovery for reliable operation of ML solutions, aligned with current regulatory requirements.
Translate business requirements into production-ready implementations through close interaction with customers and stakeholders.
Collaborate with architects, analysts, data scientists, and data engineers on data warehouses, data lakes, and/or data platforms.

Requirements

Bachelor’s or master’s degree in computer science or a related field (e.g., engineering, statistics, physics).
First practical experience with DevOps/MLOps and cloud platforms including Microsoft Azure, AWS, and/or GCP, plus Databricks.
Ability to communicate and present analytical results to management.
Interest/experience across the full data lifecycle is an advantage (cloud infrastructure, data engineering, analytics/visualization, ML engineering, and MLOps).
Fluent in English and German.

Nice to Have

Working experience with cloud technologies (AWS/Azure/GCP), Kubernetes, and programming languages such as Python, Java, and Scala.
Practical experience with SQL/NoSQL databases, data lakes.
Experience with big data (Apache Spark), data streaming (Apache Kafka), and workflow orchestration (Apache Airflow / Dagster).

About Machine Learning Reply

Machine Learning Reply is a consultancy focused on cutting-edge machine learning and data-processing projects for clients across multiple industries. The team works end-to-end across cloud infrastructure, data engineering/analytics, and ML/MLOps to deliver production-ready solutions.