EVP Global Functions
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.
More Insights
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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