Mid-level ProfessionalAs 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. 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 YOUYour profileHands-on experience with ML frameworks, libraries, agile environments, and delivering machine learning solutions using DevOps principlesKnow-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 infrastructureExperience 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 lifecycleAbility in crafting CI/CD pipelines for machine learning projectsExperience with serverless (Python, .Net) and knowledge of data warehousing systems as well as knowledge of setting up target database systemsYour responsibilitiesDeploy, 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 driftEstablish workflows for model deployment, operations, and decommissioningTrack, snapshot, and manage assets used to create modelsCollaboration, sharing, and standardization of ML pipelines developed by data scientistsMaintaining the integrity of model assets and logging access controlsSupport portability of models across a variety of platformsEnsuring model performance meets functionality and latency requirementsDevelopment, validation, and release of modelsEvaluate design patterns for model deployment, evaluate design patterns for unit testing and integration testing for machine learning productsBuild and maintain scalable ML Ops frameworks to support product-specific modelsLET'S TALK ABOUT US"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.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 INSIGHTSBe Energized. Be you. 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.
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