Cyber Defense Aws Machine Learning Ops Engineer

The Cyber Defense Machine Learning Ops (MLOps) Engineer is a member of the SIEMENS Cyber Security OperationCenter Organization, whose primary mission is to detect, analyze, investigate, and defend against sophisticated digitalattacks.

The MLOps Engineer designs, implements and operates infrastructure to support AI use cases on top of an analyticsplatform leveraging cloud and on-premises technologies.

**We make real what matters. This is your role**:

- You will be part of the Artificial Intelligence and Security Analytics (AISA) team, supporting Data Scientist.
- Design, implement and operate infrastructure that supports AI/ML use cases applied to Cyber Security.
- Integrate already developed ML models on top of a Big Data Analytics platform.
- Maintain and improve Cyber Defense ML infrastructure.
- Extend the AI cloud platform to meet the ML use case requirements.
- Build tools to track and reproduce ML experiments.
- Build and architect AI/ML platform using cloud technologies, preferably Amazon Web Services (AWS).
- Build CI/CD pipelines for Cyber Defense ML Use Cases.
- Collaborate with other SIEMENS departments and customers to accomplish your tasks.
- Applies advanced skills to resolve very complex problems not covered by existing procedures or practicesindependently.
- Displays a high level of critical thinking in bringing successful resolution to high-impact, complex, and/or cross-functional problems.
- Demonstrates and applies comprehensive knowledge of field of specialization to the successful completion ofcomplex assignments.
**Day-to-day tasks**:

- Create tools to monitor models that are already in production in order to detect phenomena like data drift or modeldecay.
- Create cloud-based infrastructure (using IaC) to serve an ML Model and deploy it.
- Automate ML models lifecycles using AWS service, e.g. using SageMaker Pipelines.**Use your skills to move the world forward**:

- BS/BA in a related discipline, or advanced degree, or equivalent combination of education and experience.Certification may be required in some areas.
- 2+ years of successful work experience in a related field or solid theoretical understanding of AI/ML operations andcloud-based systems.
- 2+ years of demonstrated experience in developing and operating on Amazon Web Services.
- Experience with:

- Mmodern ML Ops frameworks and platforms, e.g., Amazon SageMaker services.
- Infrastructure as Code, e.g., Terraform and/or CloudFormation.
- Cloud-based storage services, e.g. S3, DynamoDB, RDS, etc.
- Developing CI/CD pipelines.
- Version control tools, e.g., Git.
- Deploying ML models served using different protocols (HTTPS, MQTT, etc.), tools (Flask, FastAPI,etc.) and environments (Cloud managed services, on-prem servers, etc.
- Basic knowledge about ML development lifecycle, i.e., pre-process data, train a model, evaluate a model, tune a model, etc.
- Proven skills in Python programming language.
- Written and verbal communication skills in English.

**Preferred knowledge/skills, education, and experience**:

- Preferred knowledge or experience in several of the following areas:

- Experience with DevSecOps (e.g., static code analyzers, linting tools, cloud best practices, etc.)
- Experience in a cybersecurity environment
- Experience with data engineering, batch and stream processing tools
- Basic knowledge about ML concepts (e.g., supervised vs. unsupervised algorithms, metrics, classification vs. regression, etc.)
- AWS Certification degree is of advantage, e.g., AWS Solution Architect, AWS Developer

Organization**: Cybersecurity
**Company**: Siemens S.A.
**Experience Level**: Experienced Professional

**Job Type**: Full-time