Executive Director, AI Engineering

You desire impactful work.
 

You’re RGA ready

RGA is a purpose-driven organization working to solve today’s challenges through innovation and collaboration. A Fortune 500 Company and listed among its World’s Most Admired Companies, we’re the only global reinsurance company to focus primarily on life- and health-related solutions. Join our multinational team of intelligent, motivated, and collaborative people, and help us make financial protection accessible to all.

Position Overview

The Executive Director, Data Science Engineer leads the end-to-end development, deployment, and operationalization of both traditional machine learning and generative AI solutions across RGA’s Americas region. This role blends deep expertise in MLOps and LLMOps, enabling scalable, secure, and compliant AI systems. The incumbent will architect and maintain robust ML and LLM pipelines, ensure governance and monitoring, and collaborate cross-functionally to deliver high-impact solutions. Operating independently, this role may lead cross-functional initiatives with moderate resource requirements and complexity.

Responsibilities

• Strategic Leadership & Technical Execution
  • 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.
• Model Development & Evaluation
  • 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. Identify synergy opportunities to refactor architecture to common patterns and modular services.
• Infrastructure & Automation
  • 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.
• Collaboration & Influence
  • Partner with data scientists, actuaries, software engineers, and business stakeholders to align technical solutions with strategic goals.
  • Lead cross-functional project teams and mentor junior engineers and data scientists.
• Innovation & Thought Leadership
  • Stay abreast of emerging technologies and methodologies in AI/ML, MLOps, and LLMOps.
  • Recommend and implement innovative solutions to complex business challenges, including responsible AI practices.

Requirements

• Bachelor’s degree in Computer Science, Math, Data Science, Machine Learning, or related technical field
• 10-15 years of machine learning experience
• 3+ years of experience in MLOps and/or LLMOps, including deployment and monitoring of models in production
• Technical Proficiency
  • 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.
• Analytical & Problem Solving
  • Sophisticated analytical thought to solve complex problems and identify innovative solutions
  • Ability to interpret internal/external business challenges and recommend best practices
• Leadership & Communication
  • Proven ability to lead cross-functional teams and influence stakeholders
  • Strong written and verbal communication skills, capable of conveying complex technical concepts to non-technical audiences

Preferred

• Ph.D in Computer Science, Machine Learning, Artificial Intelligence, Computational Science or related technical field
• Advanced certifications in cloud platforms or machine learning specializations
• Knowledge of real-world data, medical, underwriting, and other third-party data and con