Quantitative AI Engineer

Overview:    The centralized Asset Modeling team provides modeling oversight of the company's entire general account portfolio which includes the configuration, validation, valuation and financial projection of fixed income and derivatives in support of the company's financials. Some of our financial reporting includes statements, regulatory filings, internal short and long-term projections, stress testing and M&A activity.  The team partners closely with Investments, Trading, ALM, Finance, and Accounting, as well as the liability counterparts in the Actuarial department.   Our Quantitative AI Engineer position will enable asset modeling foundations with hands-on artificial intelligence (AI) engineering strategies for the future of our organization. The strategy will allow us to modernize how Talcott values, projects, and explains its general accounts. The Quantitative AI Engineer will deliver generative and agentic AI capabilities that accelerate model production, automate documentation and controls. They will also provide self-service applications including chat experiences for investments, ALM, finance, and risk users while remaining grounded in fixed income, derivatives, and ALM analytics. This opportunity will sit within the “AI Lab” of the actuarial department while supporting AI-driven business applications, asset modeling, and engineering.  The ideal candidate will work on a hybrid in-office schedule at either our Hartford, CT office or our Charlotte, NC office.   Responsibilities:     Generative & Agentic AI for Business Applications   Design and deploy GenAI copilots and agentic workflows for asset modeling, ALM, finance, and risk users, enabling natural-language queries, multi-step model execution, reconciliation, and exception handling. Build self-service chat tools, LLM (large language model) based auto-documentation for governance and audit, AI-assisted reconciliation and anomaly explanation, and RAG (retrieval-augmented generation) solutions grounded in actuarial methods, regulatory guidance, and prior results. Partner with Risk, Compliance, and IT to establish AI governance, safety, validation, and human-in-the-loop controls. Stay up to date with technological advancement in AI tools and applications, and continuous development of potential use cases for the company.    AI/ML for Asset Modeling & Optimization   Develop ML (machine learning) models for prepayment, credit migration and defaults Apply Bayesian and ML (machine learning) optimization for SAA, hedging, capital efficiency, and surplus generation Implement anomaly detection for valuation QA and model validation.    Engineering & Production   Build and maintain Python services integrating AXIS, KRM, QuantLib, and internal platforms Follow best practices in version control, CI/CD (continuous integration/continuous deployment) and code review Contribute to validation, controls and reconciliation frameworks.                                                                Qualifications:    Bachelor’s Degree is required with preference for majors in quantitative finance, mathematics, statistics and AI; a Master’s Degree is a plus! Minimum of 2 years of experience with AI applications in insurance and investments  Strong mathematical and analytical skills with working knowledge of fixed income asset classes, pricing models, complex derivatives, and numerical pricing techniques.  Hands-on experience building AI/ML applications in a business setting to include Generative AI, agentic workflows, chat assistants, or production ML Technical experience requirements: Python (strong expertise,) NumPy, pandas, scikit-learn, FastAPI.  GenAI / Agentic AI: LLM APIs (OpenAI, Azure OpenAI, Anthropic), prompt engineering, RAG, vector databases, LangChain, LangGraph, Semantic Kernel, AutoGen or similar agent frameworks.  ML toolkit: scikit-learn, XGBoost; familiarity with PyTorch or TensorFlow.  Quantitative library knowledge: QuantLib familiarity is strongly preferred.  Cloud exposure (Azure preferred) for AI services Demonstrated ability to take ownership of processes and drive improvements independently Experience providing project oversight or leading components of projects is a plus Strong communication skills, with the ability to translate complex analysis into clear, actionable insights for senior stakeholders Attention to detail and ability to manage multiple deliverables Strong analytical and problem-solving skills, with demonstrated experience working with complex datasets and reporting frameworks Results-oriented with a demonstrated ability to work under tight deadlines in a high-performance environment.