AI ML Engineer

AI ML Engineer

AI ML Engineer

Are you passionate about building AI solutions that make a real-world impact at scale? Do you thrive at the intersection of cutting-edge machine learning, cloud-native platforms, and enterprise-grade engineering? If so, Swiss Re wants to hear from you!

About the Role

Join Swiss Re's GDS AI & Machine Learning Operations (MLOps) team as an AI ML Engineer and be at the forefront of shaping the future of data-driven decision-making in one of the world's leading reinsurance companies. In this role, you will design, build, deploy, and operate AI and Machine Learning solutions that deliver measurable business value at scale. You will collaborate with data scientists, platform engineers, and business stakeholders across the globe to accelerate AI adoption, drive operational excellence, and push the boundaries of what's possible with modern AI technologies.

This is your opportunity to work with cutting-edge Generative AI, Large Language Models, cloud-native platforms, and enterprise-scale ML systems — all while contributing to Swiss Re's mission to make the world more resilient.

Key Responsibilities

• AI Solution Engineering & Lifecycle Management: Design, develop, deploy, evaluate, monitor, and continuously improve machine learning models, Generative AI applications, and AI agents in close collaboration with data scientists, platform engineers, and business stakeholders

• Data & AI Pipelines: Build and maintain scalable data, ML, and AI workflows, including batch and real-time processing pipelines, to support enterprise-scale AI use cases

• MLOps, LLMOps & AIOps: Implement MLOps, LLMOps, and AIOps best practices for model deployment, evaluation, observability, automation, monitoring, drift detection, and operational excellence

• AI Platform Engineering: Develop, operate, and enhance AI platforms and services, ensuring high availability, scalability, reliability, and a seamless user experience for AI practitioners and business teams

• AI Governance, Security & Compliance: Support responsible AI practices through model governance, red teaming, security reviews, risk assessments, and compliance controls across the AI lifecycle

• Business Enablement & Collaboration: Partner with business stakeholders, data scientists, and engineering teams to identify opportunities, onboard AI use cases, and deliver measurable business value through AI solutions

• Observability & Production Operations: Establish monitoring frameworks, dashboards, alerting, and operational processes to ensure the reliability, performance, and supportability of AI applications in production

• Cost & Capacity Optimization: Optimize AI infrastructure utilization, model performance, and operational costs while supporting capacity planning and sustainable AI adoption across the organization

About the Team

GDS (Group Data Services) leads Swiss Re's ambition to be a truly data-driven risk knowledge company, bringing expertise and experience across all aspects of data and analytics to enable Swiss Re's vision of making the world more resilient.

Within GDS, the Data Platform Engineering team is responsible for delivering and operating data & analytics platforms that power innovative, data-driven business solutions. The team ensures Swiss Re Group can efficiently leverage these platforms, maintaining their availability, performance, and continuous evolution.

At the heart of this sits the GDS AI & Machine Learning Operations (MLOps) team — an agile, collaborative, and globally distributed group operating across India, Slovakia, and Switzerland. We work with cutting-edge AI technologies, cloud-native platforms, and enterprise-scale ML systems, serving an internal customer base that spans every continent. We are passionate about what we do, committed to quality, and genuinely enjoy the journey of making advanced AI accessible and impactful across the organisation.

About You

You are a curious, driven, and collaborative AI/ML professional who thrives in fast-paced, innovative environments. You bring strong technical depth combined with excellent communication skills, and you are energised by working with diverse, global teams. You have a genuine passion for AI technologies, a customer-first mindset, and the persistence to make emerging technologies accessible to a broader community — even as they continue to evolve. You take pride in your work, value continuous learning, and understand that enjoying the journey is just as important as delivering results.

We are looking for candidates who meet these requirements:

• 5+ years of experience in machine learning engineering, MLOps, AI platform engineering, or related fields, including hands-on deployment, monitoring, and operation of AI/ML solutions in production environments

• Strong proficiency in Python and experience developing AI/ML applications using frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, Semantic Kernel, or similar technologies

• Hands-on experience with MLOps, LLMOps & Observability, including model deployment, CI/CD pipelines, model monitoring, drift detection, performance evaluation, observability, logging, and alerting

• Cloud & Platform Engineering experience, particularly with Microsoft Azure, Azure AI Services, Azure OpenAI, containerization (Docker, Kubernetes), and infrastructure automation

• Generative AI & LLM expertise, including RAG architectures, prompt engineering, AI evaluation frameworks, AI agents, model benchmarking, and enterprise AI workflows

These are additional nice to haves:

• Experience with Palantir Foundry or willingness to learn and work with Swiss Re's strategic data platform

• Familiarity with distributed data processing technologies such as Apache Spark and experience building scalable data pipelines supporting AI and analytics workloads

• Knowledge of Responsible AI, AI governance, security controls, model risk management, red teaming, and compliance practices in enterprise AI adoption

• Experience with containerization and automation tools such as MLflow, GitHub Actions, Azure DevOps, or similar platforms used to manage AI/ML workloads

• Prior experience in insurance, reinsurance, financial services, or other highly regulated industries

• Experience supporting production AI systems, platform operations, or customer-facing AI solutions

• A Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Software Engineering, or a related field

• Strong analytical and problem-solving skills with the ability to troubleshoot complex AI, platform, and operational challenges

• Excellent stakeholder management and communication skills, with the ability to collaborate effectively across global, cross-functional teams

Our company has a hybrid work model where the expectation is that you will be in the office at least three days per week.