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About This Role
Job Details
---------------
Location:
Columbus, OH
Category:
Information Technology
Employment Type:
Full time, Remote
Job Ref:
R2626243\-174
Principal Software Engineer \- IE06GE
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.
At the Hartford, we are seeking a Principal AI Engineer who is responsible for building our AI platform to accelerate designing, developing, and deploying AI use cases, and drive innovation at scale, with particular emphasis on GenAI capabilities and acceleration of use cases.
We are driven by a strong determination to create a meaningful impact and take pride in being an insurance company that extends far beyond the realms of policies and coverages. When you choose to be a part of our team, you open the door to endless opportunities for personal and professional growth, as well as the chance to empower others in reaching their aspirations. You will help bring the transformative power of AI/GenAI capabilities to re\-imagine the ‘art of possible’ and serve our internal customers and transform the businesses.
This team is dedicated to building state\-of\-the\-art AI platform. We are looking for an experienced Principal AI Engineer, to help us build the foundation of our AI capability. You will be a thought partner to the platform leadership. You will provide vision and impeccably execute the build of platform capabilities that accelerates the build of complex digital assistants, enables rapid experimentation, supports scaling and productionization of the solutions with built in scalability, security and self\-service features. The ideal candidate is an accomplished professional with significant hands\-on experience in data, analytics, MLOps/LLMOps engineering who is equipped to manage and drive broader stakeholder engagements.
This role requires versatility and expertise across a wide range of skills. Someone who is biased for action, visionary and an engineer at heart will fit into this role seamlessly.
This role can have a Hybrid or Remote work arrangement. Candidates who live near one of our office 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 will have a remote work arrangement, with the expectation of coming into an office as business needs arise. Candidate must be authorized to work in the US without company sponsorship.
Responsibilities:
- Key thought leader, collaborator, and contributor to the build of the AI platform that supports and accelerated GenAI and traditional predictive and ML solutions.
- Define and execute the vision and roadmap for AI platform that align with the organization's strategic goals and customer needs.
- Drive the future state of the AI platform by staying updated with the latest trends and developments
- Lead the build and execution of POCs and prototypes that serve to qualify and validate emerging AI platform solutions that can be leveraged as solution quick start and eventually become capabilities within the AI platform for enterprise consumption.
- Provide hands\_on engineering support to the build of platform capabilities e.g., agentic frameworks, IAC, production grade capabilities, monitoring systems, developer experience, FinOps capabilities etc
- Partner with our shared service teams like Architecture, Cloud, Security, etc to design and implement platform solutions.
- Collaborate with the DS team to develop a self\-service internal developer AI platform.
- Provide leadership and mentorship to engineers on the team, fostering a culture of innovation, collaboration, and continuous upskilling and learning.
Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, or a technical field.
- 10\+ years of experience with cloud (AWS / GCP)
- Demonstrate a willingness to challenge the status quo and a bias for action
- Extensive programming experience with Python, Typescript.
- At least 8 years of experience designing and building data\-intensive solutions using distributed computing.
- 10\+ years building and shipping software and/or platform infrastructure solutions for enterprises.
- Hands\-on experience with cloud platforms and services, such as Google Cloud, AWS, Azure and their offerings for AI / generative AI
- Experience with CI/CD pipelines, Automated Testing, Automated Deployments, Agile methodologies, Unit Testing and Integration Testing tools.
- Experience with building scalable serverless application (real\-time / batch)
- Knowledge of distributed NoSQL database systems.
- Experience with data engineering, ETL technology, and conversation UX is a plus.
- Experience with HPCs, vector embedding, and Hybrid/Semantic search technologies.
- Proficiency in customization techniques across various stages of the RAG pipeline, including model fine\-tuning, retrieval re\-ranking, Hybrid search and multimodal RAG plus.
- Strong proficiency in embeddings, ANN/KNN, vector stores, quantization, database optimization, \& performance tuning.
- Experience in building Agentic system using frameworks like LangGraph and crewai is a plus.
- Basic understanding of Natural Language Processing, vector space models and Deep Learning.
- Excellent problem\-solving skills and the ability to work in a collaborative team environment.
- Excellent communication skills.
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:
$168,400 \- $252,600
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
Salary Context
This $168K-$252K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $168K to $252K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
The Hartford AI Hiring
The Hartford has 4 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Columbus, OH, US, Chicago, IL, US. Compensation range: $175K - $273K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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