Allianz Services is proud to serve and be part of Allianz Group, one of the world’s leading insurers and asset managers. Our global footprint, with more than 7,300 employees located across ten countries and four continents, enables us to unlock value for our partners across the insurance value chain and deliver superior client experience within Allianz Group. At Allianz Services, we have a strong specialization in core insurance operations, shared services, business analytics, engineering, and management consulting. Our people and our trusted relationships with partners are what matter most. These are rooted in our values of compassion, integrity, and expertise and we strive to continuously evolve, improve, and deliver excellence while contributing successfully to the transformation of Allianz. Caring actively for the environment, for people, and for our customers is what makes us a great place to work and together we are shaping a better and more caring tomorrow.
Allianz Services is a company which belongs to the legal entity, Allianz Technology SE.

PREFERRED QUALIFICATIONS:

• At least 5 years of hands-on experience as part of end-to-end ML projects.
• Expertise in technical documentation practices (e.g. Arc42).
• Knowledge of continuous monitoring of performance of ML applications and tools and environments (Grafana, Prometheus, Kubernetes, CI-CD, etc.).
• Advanced understanding of cloud technologies (AWS and Azure).
• Good understanding of technical feasibility of data-driven products and services.
• Experience coordinating with various technical stakeholders (Engineers, Architects, Data Scientists) to achieve a common goal.
• Strong ability to self-organize, take ownership of topics and drive them to delivery together with others team members.
• Experience in monitoring data drift in a running ML system.
• Insurance knowledge and additional languages are a plus.

JOB ROLE

We are seeking a skilled professional to develop and deploy Machine Learning (ML) and GenAI applications, focusing on operational efficiency and monitoring. The role involves addressing bottlenecks, collaborating with product teams, and ensuring smooth adoption of ML workflows. You will follow industry best practices such as Agile and DevOps, handle software engineering tasks (design, development, testing, deployment), and contribute to technical documentation. Additionally, mentoring junior colleagues and coordinating with cross-functional teams are key responsibilities.

WHAT YOU WILL DO

• Enable the successful development and deployment of Machine-Learning and (Gen)AI applications from conceptualization to production with an emphasis on operations and monitoring.
• Detect, analyze, address bottlenecks and pain points along the ML workflow with patterns and practices that will improve quality and speed.
• Listen and engage with the product team members to ensure their adoption in a collaborative and influential fashion so that siloed behavior is prevented.
• Follow standard industry processes such as Agile, DevOps, version control, model management, deployment and operation of ML applications.
• Keep hands-on performing software engineering tasks such as: requirements analysis, design, development, testing, deployment, code maintenance, data pipelines, etc.
• Contribute and review architectural and other technical documentation, acting as a sparring partner.
• Mentor / train more junior colleagues in areas of expertise.