Technical Data Specialist

vor 6 Monaten

Munich, Deutschland Munich Re Vollzeit

Technical Data Specialist (Advanced Analytics) (M/F/d)*

**Company**
Munich Re
**Location**
Munich, Germany

for the TALARIA team as part of EULA5.

Would you like to be part in an exciting new business model?

Digitalisation, Big Data, AI or ML are paving the way for new business models and innovative risk transfer solutions - which are key pillars for generating profitable growth in the PI and RI segment.

Innovation is a key pillar of Munich Re’s strategy, and with TALARIA, the primary goal is to open up the European SME market for receivables financing and then to venture into additional world regions. TALARIA integrates into existing platforms used by SMEs, e.g. for accounting or invoice management and enable these platforms to offer receivables financing and insurance solutions with just a few clicks. Our vision is to contribute to the growth and sustainability of the SME segment in Europe and beyond. Our approach is based on client centricity, technology, renowned technical skills and renowned internal and external expertise of our cooperation partners.

Talaria operates in Germany and Spain and is looking to expand into other European countries.

We are looking for a highly skilled and motivated Data/ML Engineer to join our international and dynamic team.

As an ML Engineer, you will play a critical role in designing and implementing robust and scalable machine learning operations (MLOps) solutions. You will work closely with data scientists, software engineers, and other stakeholders to ensure efficient and effective deployment and management of machine learning models in our production environment. This is a hands-on technical role that requires a deep understanding of MLOps best practices, Azure cloud services, Databricks, stakeholder management, NLP Frameworks, and proficiency in Spark, Python, and CI/CD.

This position offers an exciting opportunity to lead initiatives at the intersection of data engineering and AI, utilizing state-of-the-art technologies such as Databricks and Azure AI to drive innovation and deliver impactful solutions.

**Your job**:

- Designing, developing and implementing end-to-end MLOps pipelines, ensuring the seamless integration of machine learning models into production environments.
- Collaborating with data scientists, software engineers, and other stakeholders to understand their requirements and translate them into scalable and reliable MLOPS solutions.
- Architecting and implementing data pipelines for feature extraction, preprocessing, and transformation using technologies such as Spark and Python.
- Developing and maintaining a scalable feature store to enable efficient feature reuse across multiple machine learning models.
- Establishing and enforcing best practices for model versioning, model deployment, and monitoring.
- Implementing continuous integration and continuous deployment (CI/CD) pipelines for efficient and automated model deployment and updates.
- Working closely with infrastructure and DevOps teams to ensure the availability, scalability, and security of MLOps systems.
- Collaborating with stakeholders to define and track key performance indicators (KPIs) and develop monitoring and alerting systems to ensure the quality and reliability of deployed models.
- Ensuring a high level of performance, security, scalability, appropriate reusability and reliability with your solution design.
- Staying up to date with the latest advancements in MLOps technologies and methodologies and provide recommendations for process improvements.
- Delivering compelling presentations, product demos, roadmap reviews, sample solutions and discussions to both technical and non-technical stakeholders to drive the adoption of analytics and AI.

**Your profile**
- University degree, preferably in Computer science / IT or similar qualification.
- 5+ years of experience in data engineering and AI Development.
- Highly developed analytical and conceptual skills combined with the ability to present complex situations in a clear and understandable way.
- Extensive experience in designing and implementing MLOps solutions in an Azure and Databricks cloud environment.
- Strong understanding of Data engineering and ML concepts and hands-on experience with deploying machine learning models.
- Proficiency in programming languages such as Python (Frameworks like NumPy, Scikit-learn, Tensorflow, keras) and Spark for data processing, feature engineering, and model deployment.
- Taking responsibility for using cloud technology, machine learning and data to enable the productivity.
- Solid understanding of CI/CD practices and experience with relevant tools and technologies (e.g., Azure DevOps, Jenkins, Git).
- Strong problem-solving and troubleshooting skills to identify and address issues in MLOps pipelines.
- Knowledge of containerization technologies (e.g., Docker, Kubernetes) and orchestration frameworks is a plus.
- Excellent stakeholder management and communication skills, with the

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