DevOps/ML Engineer (m/f/d)
At a glance
Mid-level Machine Learning Engineering role at Machine Learning Reply. Munich · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build technical approaches for data-heavy applications centered on machine learning and AI
- Deploy and own customer solutions on cloud platforms or on-premises infrastructure
- Automate repeatable work with DevOps and MLOps practices to shorten delivery time
- Set up monitoring, failover and recovery so customer ML systems run safely and meet regulatory needs
- Turn customer requirements into production systems with architects, analysts, data scientists and engineers
What you bring
- A bachelor's or master's degree in computer science or a related discipline such as engineering, statistics or physics
- Some hands-on exposure to DevOps or MLOps and computing platforms including Azure, AWS, GCP or Databricks
- Fluent German and English, plus the ability to explain analytical findings to management
Who this fits
This mid-level consulting role suits someone who enjoys coding, data and AI and wants to deliver machine learning and data-processing solutions for clients. You’ll work across disciplines and can split your work among client sites, the Munich office and remote settings.
From the employer
At Machine Learning Reply, we work with our customers on cutting-edge projects for which we are looking for DevOps and ML Engineers to support our customer projects around machine learning and data processing across various industries. To expand our team, we are looking for a talented and highly skilled consultant with a technical background to join our team. As a consultant, you will be responsible for providing expert advice and technical support to our clients.
If you're a DevOps or ML Engineer or just starting out in the field of machine learning and/or DevOps engineering - if you never lose focus, love coding, data and AI, and are passionate about bringing your ideas to life - then we want to hear from you!
Responsibilities:
Design innovative, technical approaches for data intensive and applications with a focus on machine learning and artificial intelligence.
Implement and take ownership for your solutions on in either cloud based (AWS, Azure or GCP) and/or on-premises infrastructures of our customers.
Automate recurring tasks by state-of-the-art DevOps and MLOps concepts, enabling your customers to significantly reduce their time-to-delivery.
Take care of the necessary monitoring, failover, and recovery infrastructures that allow our customers save operation of their machine learning solutions in agreement with latest regulatory requirements
Closely interact with customers and stakeholders to translate concrete and complex business requirements into production-ready solutions
Collaborate with various disciplines such as enterprise architects, analysts, data scientists or data engineers to develop data-intensive applications such as data warehouses, data lakes and/or data platforms
What we offer you:
Access to work on projects across industries (large and mid-market companies in Banking, Insurance, Automotive, Retail, etc.)
Broaden your skills through interdisciplinary work and training in the areas of data engineering, cloud architecture, and data science
Benefit from industry-leading cooperations in the cloud, BI, and AutoML field
Very active social program - including training, conferences, team buildings, Reply Exchange, communities of practices, and hackathons
Work in an open, flat environment, within a broad Reply knowledge-sharing network
Award-winning office space in downtown Munich with access to “Stammstrecke”
You choose your state-of-the-art equipment
Public transport ticket with Deutschlandticket
Gym-membership subsidy for a gym of your choice
Flexible work environment between client, Reply office, and remote work
Minimum Job Requirements / Qualifications:
Bachelor’s / master’s degree in computer science or any other related fileds (e.g. engineering, statistics, physics).
First practical experience with DevOps/MLOps principals and computing platforms like Microsoft Azure, AWS and GCP as well as Databricks.
Ability to convincingly communicate and present analytical results to management.
We cover the full lifecycle of Data, from Cloud Infrastructure, Data Engineering, Data Analytics, and Visualization to ML Engineering to and MLOps. Interest and/or experience in some of those fields is an advantage.
Fluent in English and German.
Desired:
Working experience in cloud technologies (AWS, Azure or GCP), Kubernetes and programming languages like Python, Java and Scala.
Practical experience with SQL and NoSQL database technologies and data lakes
Experience with big data technologies (Apache Spark), data streaming (Apache Kafka) and workflow orchestration (Apache Airflow, Dagster)