SimCorp Dimension (SCD) Specialist

About the position 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. Please note that the internal job title for this position is Senior Application Developer. Responsibilities Implementing end to end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment, and monitoringDesigning and implementing machine learning pipelines that support high performance, reliable, scalable, and secure ML workloadsDesigning scalable ML solutions and MLOps architectures using AWS and/or Azure services, and leveraging GenAI solutions where applicableCollaborating with cross functional teams (Applied Science, DevOps, Data Engineering, Cloud Infrastructure, Application Teams) to prepare, analyze, and operationalize data and AI/ML modelsServing as a trusted advisor to internal stakeholders and business partners on AI/ML, GenAI solutions, and cloud architecturesSharing knowledge and best practices through mentoring, training, publications, and the creation of reusable artifactsEnsuring solutions meet industry standards and supporting the advancement of enterprise AI/ML, GenAI, and cloud adoption strategiesRequirements Bachelor’s, Master’s, or PhD in Computer Engineering, Information Technology, or a related field6+ years of experience in cloud architecture and implementation and/or applied research7+ 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 SageMaker, or similar services)3+ years of experience with SQL, Python, and at least one additional programming language (e.g., Java, Scala, JavaScript, TypeScript)Proficiency with industry leading ML frameworks such as TensorFlow and PyTorchStrong communication and collaboration skills, with the ability to work effectively with senior leaders and stakeholdersAbility to build strong business relationships, negotiate effectively, and confidently articulate technical viewpointsHands 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, DevOps 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, TensorFlow, NumPy, SciPyStrong communication skills with the ability to explain complex technical concepts to both technical and non technical audiencesProven experience building ML pipelines with best practice MLOps, including data preprocessing, feature engineering, model hosting, hyperparameter tuning, distributed and GPU training, deployment, monitoring, and retrainingExperience with MLOps platforms (e.g., MLflow, Kubeflow) and orchestration tools (e.g., Azure Data Factory pipelines, Azure Functions, AWS Step Functions)Experience building applications using Generative AI technologies, including LLMs, vector databases, orchestration frameworks (e.g., LangChain), and prompt engineeringExperience developing Infrastructure as Code (e.g., CloudFormation, CDK, Terraform), containerized workloads, and CI/CD pipelines.Benefits An engaging and collaborative environment that promotes continuous learning and developmentA hybrid work environment that combines weekly in-office and remote daysA great compensation package including annual company bonusMarket leading company-paid flexible health and dental benefits, starting on your first dayFlexible dollars provided by the company to put towards Health Spending Account and/or Wellness Spending AccountImmediate participation in DC Pension Plan with an automatic employer contribution, plus optional company matchGenerous time off including vacation, personal days, unplanned time, Statutory Holidays and company-wide early closure half-daysLearning and development programs and resources, including unlimited access to LinkedIn Learning, Education Assistance Program and reimbursement for professional feesMaternity, Parental & Adoption Leave top-up programEmployee Referral Program, Recognition & Rewards Platform