Siemens Energy

As a Machine Learning Operation Engineer (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.

Let’s Talk About You!

What you bring

• Hands-on experience with ML frameworks, libraries, agile environments and deploying machine learning solutions using DevOps principles.
• Excellent knowledge of data science programming languages (Python, R, Scala).
• Excellent knowledge of the boto3 AWS SDK or additional SDKs for other cloud platforms.
• Good knowledge of cloud infrastructure.
• Excellent knowledge of container technologies (docker, Kubernetes, OpenShift etc.).
• Familiar with REST API protocol as well as at least model serving technologies (MLFlow, Seldon Core, Kubeflow, TFX, Sagemaker endpoints etc.).
• Excellent knowledge of the ML life-cycle.
• Experience in creation of CI/CD pipelines for machine learning projects.

How you will make an impact

• Deploy, operationalize and maintain Machine Learning (ML) models with a focus on optimization of model hyperparameters, automated retraining and model training, version control and governance and model monitoring and its drift.
• Establish model onboarding, operations, and decommissioning workflows.
• Track, snapshot & manage assets used to create the models.
• Enable collaboration, sharing and standardization of ML pipelines developed by data scientists.
• Maintain model asset integrity & persist access control logs.
• Certify model behavior meets regulatory & adversarial standards. You will be heavily supported by data scientists and Subject Matter Experts in this task
• Support model portability across a variety of platforms. We do not have cloud-agnostic ML pipelines, but dependencies should be minimal.
• Certify model performance meets functional and latency requirements.
• Evaluate design patterns for model deployment. Evaluate design patterns for unit testing and integration testing for machine learning products.
• Create and maintain scalable ML Ops frameworks to support product-specific models.

Let’s Talk About Us!

At Siemens Energy, we are more than just an energy technology company. We meet the growing energy demand across 90+ countries while ensuring our climate is protected. We provide the power to bring heat and light to our cities. We help our customers to save millions of tons of CO2 each year. That way we not only contribute, but actively drive the energy revolution for a better and greener future.

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.

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Lucky for us, we are not all the same.

Through diversity, we generate power. We run on inclusion and compassion. Our combined energy is fueled by at least 130 nationalities. Siemens Energy celebrates character – no matter what ethnic background, gender, age, religion, identity, or disability.

Let’s make tomorrow different today!

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