Interested in this AI/ML Engineer role at Talcott Financial Group?
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About This Role
Talcott Financial Group\* is an international life insurance group and the industry’s trusted partner for comprehensive risk solutions. Talcott creatively designs and expertly delivers responsive solutions that transfer risk and manage capital in a way that supports the strategic needs of insurers today and into the future.
Talcott Financial Group has a proven track record of well\-executed transactions, and the enterprise benefits from its strong financial position with over $127 billion in assets under management, its investment\-grade financial strength ratings, and its partnership with Sixth Street, a leading global investment firm.
Talcott Financial Group’s two core business platforms include: U.S. based Talcott Resolution and Bermuda and Cayman based Talcott Re.
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.
Compensation:
This range represents the minimum and maximum annual base salary we reasonably expect to pay for this role at the time of posting. The actual base pay could vary and may be above or below the listed range. The base pay is based on factors including but not limited to experience, competence, and demonstration of proficiencies essential for the role. The base pay is just one component of Talcott’s total annual compensation for employees. Other compensation may include annual bonuses, long\-term incentives and recognition.
- This role is not eligible for visa sponsorship. Applicants must be authorized to work in the United States on a full\-time basis without current or future visa sponsorship.
\*\*Talcott Financial Group is an equal employment employer. All qualified applicants will receive consideration without regard to race, color, sex, religion, age, national origin, disability, veteran status, sexual orientation, gender identity or expression, marital status, ancestry or citizenship status, genetic information, pregnancy status or any other characteristic protected by law. Talcott Resolution maintains a drug\-free workplace. For more information regarding our Privacy Policy, please go to https://talcott.com/onlineprivacypolicy/onlineprivacypolicy.html
Salary Context
This $130K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Talcott Financial Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($155K) sits 14% below the category median. Disclosed range: $130K to $180K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Talcott Financial Group AI Hiring
Talcott Financial Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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