AI Engineer - Ceres Life Insurance

Company Overview:

Westaim and CC Capital have joined forces to strategically transform Westaim from a holding company into a global alternative credit asset manager with a unique, integrated insurance platform, branded as The Westaim Corporation. This partnership supports a long-term vision to deliver innovative, customized financial solutions across alternative credit and insurance, creating scalable growth and meaningful client impact.

Ceres USA Holdings, LLC, part of the insurance platform within The Westaim Corporation strategy, is the parent company of Ceres Life Insurance —a fast-growing, technology-driven annuity carrier startup. Ceres is focused on redefining retirement security by combining modern fintech capabilities, top-tier talent, and strong vendor partnerships to deliver exceptional annuity solutions and digital experiences.

Ceres is deeply committed to a client-centered culture. Through its Digital Contact Center and advisor-facing platforms, the company delivers proactive, personalized, and technology-enabled support that empowers clients and advisors while maintaining the highest standards of trust, security, and regulatory compliance.

Position Summary

Reporting to the Chief Innovation Officer and working alongside the Head of Data Analytics and Head of Data Engineering, the Artificial Intelligence Engineer will design, develop, and deploy generative AI and machine‑learning solutions across Ceres Life’s core pipelines and customer‑facing tools. You will embed NLP/LLM capabilities for agent portals, application and onboarding, customer service chatbots, and predictive models, ensuring our AI initiatives deliver measurable business value and operational efficiency.

Key Responsibilities

Generative AI & ML Development

• Model Selection & Fine‑Tuning: Evaluate, select, and fine‑tune large language models and specialty AI frameworks (e.g., LLMs for document summarization, transformers for risk scoring).
• Prompt Engineering: Craft and optimize prompts for diverse use cases—automated eApp summarization, policy recommendation engines, conversational assistants.
• MLOps & Deployment: Build reproducible, scalable pipelines for model training, validation, monitoring, and versioning using tools like MLflow, Kubeflow, or SageMaker.

Integration & Automation

• API Development: Create robust APIs and microservices to surface AI capabilities (chatbots, document ingestion, predictive alerts) within the agent portal and internal dashboards.
• Workflow Automation: Partner with Operations & Customer Experience to automate tasks such as licensing checks, claims triage, and commission forecasting using AI‑driven workflows.
• Continuous Improvement: Instrument A/B tests and performance metrics to monitor model accuracy, latency, and user satisfaction; iterate on models an