Interested in this AI/ML Engineer role at The Hartford?
Apply Now →Skills & Technologies
About This Role
Job Details
Location: Charlotte, NC Category: Data & Analytics Employment Type: Full time, Hybrid or Remote Job Ref: R2627133-166 Sr Data Engineer - GE07BE We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
The Employer Group Actuarial Service area is seeking a Senior Cloud Data Engineer who combines deep expertise in cloud data engineering with a passion for Artificial Intelligence (AI), automation, and modern data platforms. The ideal candidate will help design and implement scalable data solutions while leveraging emerging AI technologies, intelligent automation, and cloud-native capabilities to accelerate business insights, operational efficiency, and innovation across Employer Benefits actuarial functions.
This position is in a small group that works directly with our customers and prides itself in delivery capabilities. This role offers the opportunity to have a significant bottom-line impact through the delivery of quality and actionable information to our business customers. The ideal candidate is a highly motivated, results-oriented individual with strong technical skills and project-level leadership experience.
We are looking for a well-rounded individual that is comfortable working in a process light environment and willing to cross the traditional role boundaries of developer, BA, Architect and project lead. To be successful in this role you must be comfortable working directly with customers in an iterative fashion to achieve the desired result. As such, strong verbal communication and the ability to create your own technical specifications and designs based on customer needs is critical for success in this role.
This role can have a Hybrid or Remote work schedule. Candidates who live near one of our locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday). Candidates who do not live near an office should maintain their current work arrangement with the expectation of coming into the office as business needs arise
Responsibilities:
- Regularly engage in continuing education and serve as a technical expert in Informatica IDMC, Snowflake, AI and Generative AI platforms, cloud-native data services, CICD tooling, environment management, Oracle, metadata-driven architectures, and enterprise data quality processes.
- Evaluate, prototype, and operationalize emerging AI technologies, including Generative AI, Agentic AI workflows, Retrieval Augmented Generation (RAG), and Model Context Protocol (MCP)-based integrations.
- Design and develop high quality, scalable data solutions to enable pricing and reserving activities for Employer Benefit Actuarial team.
- Ability to synthesize, reconcile, and explain data differences between GB actuarial output against other standard published data.
- Gain deep understanding on various Actuarial applications, data elements and business process.
- Work closely with Actuarial customers during the close of current financial months to provide solutions to time sensitive issues while ensuring strategic solutions are planned for next release cycle.
- Provide technical leadership and mentorship to junior engineers while fostering collaboration across onshore and offshore teams to deliver innovative, scalable, and sustainable data solutions. Leverage strong analytical and problem-solving skills to discover, integrate, and optimize data from diverse sources, enabling informed decision-making and supporting mission-critical business processes.
- Ability to discover, analyze and integrate data from various sources to support decision making and enable critical business processes
- Consult with cross-functional team partners to understand business needs and develop reliable and effective solutions that are easily maintainable.
- Ability to work directly with the customers to understand their needs and provide solutions.
- Willingness to develop deep functional knowledge in the pricing and rating process.
- Develop and maintain scalable data pipelines and ETL processes to support Insurance processing, actuarial analysis, and reporting.
- Use Enterprise GitHub for version control, documentation, code collaboration, and technical project management.
- Research, evaluate, and champion modern data technologies, frameworks, and best practices to drive operational excellence and accelerate data-driven innovation. Build deep functional knowledge of pricing, rating, and actuarial processes while working within agile delivery frameworks and partnering across organizations to enhance enterprise data assets and capabilities.
Qualifications:
- Bachelor’s degree with at least 7 years of applicable work experience.
- Experience in following is required: Snowflake, IDMC, Informatica, Oracle
- Hands-on experience using AI-assisted engineering tools, Snowflake Cortex and Agentic AI capabilities, Generative AI platforms, Model Context Protocol (MCP), cloud-based AI services, and intelligent automation frameworks.
- Experience building AI-powered self-service analytics, intelligent data catalogs, automated reconciliation frameworks, or knowledge management solutions.
- Demonstrated passion for continuous learning in Artificial Intelligence, cloud architecture, automation, and emerging technologies.
- Experience with CICD, GitHub, Autosys, and SQL
- 7+ years ETL / Data analysis / Warehouse Technologies experience
- Strong data warehouse knowledge preferably in the insurance domain, Demonstrated expertise in Data analysis, Data Quality and reconciliation processes.
- Group Benefits business knowledge is a plus.
- Knowledge of AWS and cloud-based data solutions is a plus
- Strong experience in Python , R and other modern languages, including experience building APIs, automation frameworks, AI-driven applications, and cloud-native data services.
- Experience in on-premises to cloud migration of data pipelines, modernizing them to leverage full cloud potential.
- Experienced in Agile and Kanban Methodologies
Candidate must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$117,200 - $175,800 Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
Salary Context
This $117K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At The Hartford, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($146K) sits 31% below the category median. Disclosed range: $117K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
The Hartford AI Hiring
The Hartford has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Charlotte, NC, US, Hartford, CT. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.