Machine Learning Engineer focusing on MLOps and software engineering at flaschenpost, ensuring robust planning and operational success through ML products.
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Onsite Machine Learning Engineer – MLOps, Scalable Systems
Posted yesterday
About the role
- Senior Machine Learning Engineer at APS leading MLOps initiatives and collaborating across teams. Designing and implementing scalable machine learning solutions with a focus on real-time decision-making.
Responsibilities
- Lead MLOps Initiatives: Design, build, deploy, and monitor end-to-end ML solutions that are scalable, reliable, and secure.
- Architect for Scale & Speed: Build applications optimized for low latency on high-volume data pipelines and streaming environments.
- Advise & Innovate: Act as a thought partner to data scientists and engineering leaders, bringing deep domain expertise in ML model design and infrastructure.
- Collaborate Cross-Functionally: Work with enterprise architects, product teams, and data scientists to deliver real-world business value.
- Own Quality & Governance: Establish and maintain best practices for ML lifecycle management, including CI/CD, monitoring, testing, and documentation.
Requirements
- BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field PLUS minimum four (4) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role OR advanced degree and two (2) years directly related experience.
- Held a Machine Learning Engineer or MLOps role in a large-scale enterprise environment.
- Deep experience with modern ML models, cloud-native data platforms, and orchestration tools (e.g., Kubeflow, SageMaker, MLflow).
- Proven ability to design scalable ML architectures for streaming and batch use cases.
- A mindset for mentorship and technical leadership, with the ability to guide teams on best practices in production ML.
Benefits
- Support for veterans and spouses
- Employee assistance program
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