Responsibilities
- Design, develop, deploy, evaluate, monitor, and improve machine learning models, generative AI applications, and AI agents.
- Build and maintain scalable batch and real-time data, ML, and AI workflows.
- Implement MLOps, LLMOps, and AIOps practices covering deployment, evaluation, observability, automation, monitoring, and drift detection.
- Develop, operate, and enhance highly available and scalable AI platforms and services.
- Support AI governance, responsible AI, security reviews, risk assessments, red teaming, and compliance controls.
- Partner with data scientists, engineers, and business stakeholders to onboard AI use cases and deliver measurable business value.
- Establish monitoring frameworks, dashboards, alerting, and production operations processes for AI applications.
- Optimize AI infrastructure utilization, model performance, operational costs, and capacity planning.
Requirements
- At least 5 years of experience in machine learning engineering, MLOps, AI platform engineering, or related fields, including production deployment, monitoring, and operations.
- Strong proficiency in Python and experience with frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, or Semantic Kernel.
- Hands-on experience with MLOps, LLMOps, observability, model deployment, CI/CD pipelines, monitoring, drift detection, logging, alerting, and performance evaluation.
- Cloud and platform engineering experience with Microsoft Azure, Azure AI Services, Azure OpenAI, Docker, Kubernetes, and infrastructure automation.
- Generative AI and LLM experience including RAG architectures, prompt engineering, AI evaluation frameworks, AI agents, benchmarking, and enterprise AI workflows.
- Experience with Palantir Foundry or willingness to learn it.
- Familiarity with Apache Spark and scalable data pipelines for AI and analytics workloads.
- Knowledge of responsible AI, governance, security controls, model risk management, red teaming, and compliance practices.
- Experience with MLflow, GitHub Actions, Azure DevOps, or similar automation tools.
- Prior experience in insurance, reinsurance, financial services, regulated industries, production AI systems, platform operations, or customer-facing AI solutions is preferred.
- A bachelor’s or master’s degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Software Engineering, or a related field is preferred.
- Strong analytical, problem-solving, stakeholder management, and communication skills.
Benefits
- Hybrid work model with an expectation of working in the office at least three days per week.
- Opportunity to work with global teams and enterprise-scale AI, machine learning, cloud, and generative AI technologies.
Tech Stack
Categories
About Swiss Re
The Swiss Re Group is a leading wholesale provider of reinsurance, insurance and other insurance-based forms of risk transfer. Dealing direct and working through brokers, its global client base consists of insurance companies, mid-to-large-sized corporations and public sector clients. From standard products to tailor-made coverage across all lines of business, Swiss Re deploys its capital strength, expertise and innovation power to enable the risk taking upon which enterprise and progress in society depend. Founded in Zurich, Switzerland, in 1863, Swiss Re serves clients through a network of over 70 offices globally and is rated "AA-" by Standard & Poor's, "Aa3" by Moody's and "A+" by A.M. Best. Registered shares in the Swiss Re Group holding company, Swiss Re Ltd, are listed in accordance with the Main Standard on the SIX Swiss Exchange and trade under the symbol SREN. We're smarter together. For more information about Swiss Re Group, please visit: www.swissre.com, follow us on X @SwissRe and subscribe our YouTube channel @swissretv.