Senior Accountant - Financial Accounting & Regulatory Reporting

Position Overview

At Munich Re, you will help shape and industrialize AI and Generative AI (GenAI) capabilities that support critical decision making across insurance, risk, and reinsurance domains. As a Senior Machine Learning Engineer, you will play a key role in designing, building, and operationalizing ML solutions—working closely with data scientists, engineers, and business stakeholders to turn advanced analytics into measurable business value.

You will contribute across the end‑to‑end ML lifecycle: from data ingestion and feature engineering, to model development, deployment, monitoring, and continuous improvement. Your work will span a broad range of enterprise use cases, leveraging large‑scale, heterogeneous data and modern ML engineering practices to deliver reliable, secure, and scalable AI solutions.

As a trusted technical expert, you will help set engineering standards, guide architectural decisions, and apply industry best practices to ensure robustness, performance, and regulatory alignment. You will also stay close to emerging trends in AI and GenAI, helping Munich Re responsibly adopt new technologies in a highly regulated, impact‑driven environment.

• Implement end‑to‑end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment, and monitoring.
• Design and implement machine learning pipelines that support high performance, reliable, scalable, and secure ML workloads.
• Design scalable ML solutions and MLOps architectures using AWS and/or Azure services, and leverage GenAI solutions where applicable.
• Collaborate with cross‑functional teams (Applied Science, Dev Ops, Data Engineering, Cloud Infrastructure, Application Teams) to prepare, analyze, and operationalize data and AI/ML models.
• Serve as a trusted advisor to internal stakeholders and business partners on AI/ML, GenAI solutions, and cloud architectures.
• Share knowledge and best practices through mentoring, training, publications, and the creation of reusable artifacts.
• Ensure solutions meet industry standards and support the advancement of enterprise AI/ML, GenAI, and cloud adoption strategies.

Internal job title:
Senior Application Developer.

• Bachelor’s, Master’s, or PhD in Computer Engineering, Information Technology, or a related field.
• 6+ years of experience in cloud architecture and implementation and/or applied research.
• 7+ years of experience in data, software, or machine learning engineering, with a strong understanding of distributed computing (e.g., data pipelines, distributed training and inference, ML infrastructure design).
• 3+ years of experience developing platforms for predictive modeling, NLP, and deep learning, with a proven track record of building, hosting, and deploying ML models on cloud platforms (e.g., Azure ML, Amazon Sage Maker, or similar services).
• 3+ years of experience with SQL, Python, and at least one additional programming language (e.g., Java, Scala, JavaScript, Type Script).
• Proficiency with industry‑leading ML frameworks such as Tensor Flow and PyTorch.
• Strong communication and collaboration skills, with the ability to work effectively with senior leaders and stakeholders.
• Ability to build strong business relationships, negotiate effectively, and confidently articulate technical viewpoints.
• Hands‑on experience with AWS and/or Azure, including a broad range of AI capabilities (e.g., NLP, IDP, RAG, MLOps).
• Professional‑level certifications (e.g., Solutions Architect Professional, Dev Ops Engineer Professional).
• Experience with automation and scripting (e.g., Terraform, Python).
• Knowledge of security and compliance standards (e.g., HIPAA, GDPR).
• Experience with modeling and analytics tools such as R, scikit‑learn, Spark MLlib, MXNet, Tensor Flow, Num Py, Sci Py.
• Strong communication skills with the ability to explain complex technical concepts to both technical and non‑technical audiences.
• Proven experience building ML pipelines with best‑practice MLOps, including data preprocessing, feature engineering, model hosting, hyperparameter tuning, distributed and GPU training, deployment, monitoring, and retraining.
• Experience with MLOps platforms…