RGA is looking to solve today's challenges through innovation and collaboration by making financial protection accessible to all, and this role will lead the end-to-end development, deployment, and operationalization of both traditional machine learning and generative AI solutions across RGA’s Americas region.
Requirements
- 3+ years of experience in MLOps and/or LLMOps, including deployment and monitoring of models in production
- Advanced programming skills in Python, R, Scala, and SQL
- Experience with cloud platforms (AWS, Snowflake, Databricks) and tools like Docker, Kubernetes, MLflow, Airflow, LangChain, Hugging Face Transformers
- Strong understanding of model lifecycle management, versioning, and reproducibility
- Familiarity with infrastructure-as-code and DevOps practices
- Experience with vector databases and semantic search technologies (e.g., FAISS, Pinecone, Weaviate) for retrieval-augmented generation and scalable LLM applications.
- Proficiency in model evaluation and observability tools (e.g., Evidently AI, Prometheus, Grafana, OpenTelemetry) to monitor performance, drift, and compliance of deployed ML and LLM systems.
Responsibilities
- Lead the design and implementation of MLOps and LLMOps frameworks to support scalable AI/ML solutions across business units.
- Architect and maintain robust, secure, and reproducible pipelines for both traditional ML models and large language models (LLMs).
- Drive adoption of best practices in model governance, monitoring, and lifecycle management for both ML and generative AI systems.
- Design and implement advanced machine learning models (e.g., deep learning, time series, NLP) and generative AI solutions (e.g., LLM fine-tuning, retrieval-augmented generation).
- Drive the optimization, efficiency and scalability of data science solutions and associated deployment patterns.
- Build and manage CI/CD pipelines for ML and LLM workflows using tools such as Docker, Kubernetes, MLflow, LangChain, and Terraform.
- Integrate models into production systems via APIs and microservices, ensuring scalability and reliability.
Other
- Bachelor’s degree in Computer Science, Math, Data Science, Machine Learning, or related technical field
- 10-15 years of machine learning experience
- Sophisticated analytical thought to solve complex problems and identify innovative solutions
- Ability to interpret internal/external business challenges and recommend best practices
- Proven ability to lead cross-functional teams and influence stakeholders
