As a Machine Learning Operation Engineer (f/m/d) (ML OPS) you are part of our ED&AA Unit at Siemens Energy and work on challenging projects from all areas of the energy industry. You will transform Machine Learning models to well-engineered products fulfilling development, deployment and monitoring requirements and standards.
Passionate about the environment and climate change? Ready to be part of the future of the energy transition? The Siemens Energy Data Analytics & AI team plays a significant role in driving the energy transformation.
Honestly, we don’t have all the answers. Honestly, given the scale of the challenge we need many types of perspectives to help reimagine the future. And honestly, we can’t do it alone.
Our team is looking for innovative, enthusiastic, and versatile data, digital, and AI professionals that will drive us forward on this exciting venture.Let’s Talk About You
- Hands-on experience with ML frameworks, libraries, agile environments, and delivering machine learning solutions using DevOps principles
- Know-How in data science programming languages (Python, R, Scala)
- Excellent knowledge of boto3 AWS SDK or additional SDKs for other cloud platforms and good knowledge of cloud infrastructure
- Experience in container technologies (Docker, Kubernetes, Openshift, etc.)
- Familiar with REST API protocol and at least model serving technologies (MLFlow, Seldon Core, Kubeflow, TFX, Sagemaker endpoints, etc.)
- Excellent knowledge of the ML lifecycle
- Ability in crafting CI/CD pipelines for machine learning projects
- Experience with serverless (Python, .net) and knowledge of data warehousing systems as well as knowledge of setting up target database systems
Let’s Talk About Us
- Deploy, operationalize, and maintain machine learning (ML) models with a focus on model hyperparameter optimization, automatic retraining and model training, version control and governance, and model monitoring and drift
- Establish workflows for model deployment, operations, and decommissioning
- Track, snapshot, and manage assets used to create models
- Collaboration, sharing, and standardization of ML pipelines developed by data scientists
- Maintaining the integrity of model assets and logging access controls
- Certify model behavior to regulatory and adversary standards, Supported by data scientists and subject matter experts
- Support portability of models across a variety of platforms
- Ensuring model performance meets functionality and latency requirements
- Development, validation, and release of models
- Evaluate design patterns for model deployment, evaluate design patterns for unit testing and integration testing for machine learning products
- Build and maintain scalable ML Ops frameworks to support product-specific models
Let’s make tomorrow different today is our genuine commitment at Siemens Energy
to all customers and employees on the way to a sustainable future.
In our Business Functions
we enable our organization to reach their targets by providing best-in class services and solutions in the areas of IT, HR, Finance, Real Estate, Strategy & Technology and more.
Check out this video to see what we do here! https://bit.ly/3IfnlaR
The Data & Analytics organization has been established and designed to help Siemens Energy achieve our mission by becoming a data driven organization. Treating and using data as a strategic asset enables us to support customers in transitioning to a more sustainable world, by using innovative technologies and bringing ideas into reality.MORE INSIGHTS
Be Energized. Be you.
Lucky for us, we are not all the same. Through diversity we generate power. We run on inclusion and compassion. Our combined creative energy is fueled by at least 130 nationalities. Siemens Energy celebrates character – no matter what ethnic background, gender, age, religion, identity, or disability. We energize society. All of society.Jobs & Careers:
We value equal opportunities and welcome applications from people with disabilities.