Interested in this AI/ML Engineer role at The Hartford?
Apply Now →Skills & Technologies
About This Role
Job Details
---------------
Location:
Columbus, OH
Category:
Data \& Analytics
Employment Type:
Full time, Remote
Job Ref:
R2624862\-174
AVP Data Science \- GD05AE
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 Assistant Vice President (AVP), Applied AI leads data science, traditional machine learning, and agentic AI capabilities supporting The Hartford’s Business Insurance. This role partners closely with underwriting, product, actuarial, and technology leaders to deliver scalable, production ready models and ‑AI driven‑ decision systems that support complex risks, bespoke products, and profitable growth across specialty markets.
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
This role can have a Hybrid or Remote work schedule. 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. Must be eligible to work in the US without company sponsorship.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Primary Job Responsibilities
--------------------------------
- Own delivery, performance, and risk outcomes for one or more large, complex Applied AI portfolios spanning multiple teams, domains, or lines of business. Translate enterprise and business‑unit AI priorities into multi‑year portfolio roadmaps and investment plans.
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Ensure applied AI solutions deliver measurable business value while meeting standards for security, reliability, explainability, fairness, safety, and cost efficiency across solution types including generative and agentic AI, retrieval‑augmented systems, forecasting, recommendation systems, anomaly or fraud detection, and multimodal use cases.
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Lead and develop Sr. Directors and Directors. Build leadership bench strength through succession planning, coaching, and capability development. Ensure consistent application of the Applied AI operating model, decision rights, delivery discipline, and escalation paths across the portfolio. Reinforce shared expectations for quality, evaluation rigor, and production readiness.
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Provide portfolio‑level technical direction and rigorous oversight, partnering closely with Principal ICs, Architecture, AI Platform, and Centers of Excellence. Ensure consistent adoption of approved AI standards, patterns, and guardrails.
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Review and thoughtfully evaluate portfolio‑level architectural choices, evaluation approaches, production readiness, and operational risk signals, guiding leaders through disciplined trade‑offs across quality, grounding, latency, cost, scalability, and regulatory risk.
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Accountable for consistent application of evaluation and monitoring practices across the portfolio. Ensure evaluation frameworks span classification, information retrieval, RAG/chat, forecasting, and customer or operational KPIs. Oversee governance of metric taxonomies, thresholds, validation evidence, gold and synthetic test sets, A/B testing practices, drift detection, failure‑mode analysis, and incident response expectations. Ensure evaluation results inform prioritization, release decisions, and risk management at the executive level.
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Set portfolio‑level expectations and governance for unstructured data and retrieval practices, including document ingestion pipelines, parsing, OCR, layout‑aware extraction, metadata and lineage management, access controls, PII detection and redaction, and auditability. Ensure retrieval strategy decisions, including embedding approaches, hybrid and dense retrieval patterns, reranking, grounding validation, and multilingual considerations, align with enterprise standards and regulatory requirements.
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Accountable for portfolio\-level AI governance ensuring alignment with Legal, Compliance, Model Risk, Privacy, Security, and Audit partners. Maintain readiness for audits and regulatory review by ensuring governance artifacts, controls, escalation paths, and operational evidence are consistently established and enforced. Escalate material risks, trade‑offs, and investment decisions to VPs with clear options and implications.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Partner with senior leaders across Product, Technology, Operations, Claims, Underwriting, Finance, and HR to align Applied AI delivery with business outcomes. Influence portfolio funding, prioritization, and workforce planning through evidence‑based assessments of delivery performance, evaluation outcomes, and risk considerations.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Oversee portfolio‑level planning, dependencies, resourcing, and financial stewardship. Adjust plans to address shifting priorities, capacity constraints, emerging technical risks, or regulatory changes. Drive continuous improvement in delivery effectiveness, operational resilience, governance maturity, and value realization across the Applied AI portfolio.
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Skills
----------
- Demonstrated experience leading large, complex Applied AI portfolios in regulated enterprise environments.
--------------------------------------------------------------------------------------------------------------
- Proven ability to lead Sr. Directors and Directors, building durable leadership capacity and consistent operating discipline across organizations.
------------------------------------------------------------------------------------------------------------------------------------------------------
- Strong technical and regulatory fluency across applied AI, including generative and agentic AI, retrieval‑augmented systems, evaluation and monitoring practices, and production AI operations, sufficient to review, inform, and govern senior‑level decisions.
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Applied understanding of unstructured data and retrieval approaches, including document ingestion pipelines, OCR, layout‑aware extraction, embeddings, hybrid and dense retrieval, reranking, metadata and lineage management, and PII controls.
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Deep familiarity with AI governance, model risk management, responsible AI practices, and compliance‑by‑design expectations.
--------------------------------------------------------------------------------------------------------------------------------
- Demonstrated success translating strategy into coordinated execution and investment decisions across multiple teams over multi‑year horizons.
-------------------------------------------------------------------------------------------------------------------------------------------------
- Ability to influence VPs and senior partners through clear, data‑driven communication of technical trade‑offs, evaluation outcomes, portfolio risks, and business impact.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Education, Experience, Certifications and Licenses
------------------------------------------------------
- 12\+ years of applicable experience with a Bachelor’s degree; fewer years may be accepted with a higher degree. Master’s or Ph.D. preferred in Machine Learning, Applied Mathematics, Data Science, Computer Science, or a similar analytical field, or progress towards a relevant professional designation.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- 7–10\+ years leading leaders, large portfolios, or complex programs.
------------------------------------------------------------------------
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:
$182,400 \- $273,600
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
Salary Context
This $182K-$273K range is above the 75th percentile 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($228K) sits 6% above the category median. Disclosed range: $182K to $273K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.