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About This Role
Job Details
---------------
Location:
Hartford, CT
Category:
Information Technology
Employment Type:
Full time, Remote
Job Ref:
R2625779\-168
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.
The Hartford’s applied AI COE Team is seeking a Principal AI Engineer \- Agent Ops/SRE.
The AI\-COE serves as a centralized function to accelerate AI maturity, eliminate silos, and streamline AI adoption through standardized processes and technologies, ensuring strong alignment with business objectives. AI\-COE is responsible for building reusable AI solutions, horizontal AI Agents, Agent Starter Packs and partnering with other IT/Business unit to deliver and maintain their AI Assets.
This role will support data scientist and AI solution engineer to build, deploy \& maintain AI\-COE products, ensuring reliability, uptime and throughput as per application tiers. This is the most senior individual contributor role, responsible for driving efficiency across the AI delivery lifecycle (AgentOps), while applying strong software and systems engineering practices to scale, operate, and ensure reliability of AI systems (SRE).
You will work very closely with AI platform , Cloud Engineering, Security and Enterprise SRE team using their standards and tooling to deploy and maintain the solution.
This role requires versatility across DevOps, MLOps, AgentOps, and SRE, with strong expertise in automation and a proven track record of building and operating large\-scale, mission\-critical systems.
*If you are passionate about automation and building large\-scale distributed systems, we’d love to connect with you.*
Responsibilities:
- Serve as technical liaison between AI COE and Platform Engineering \& Enterprise SRE teams.
- Ensure AI systems meet requirements for performance, latency, throughput, resiliency, recovery, observability and reliability.
- Partner with AI engineers, Applied AI Scientist, and AI Architects to design, build and maintain scalable, fault tolerant AI systems as per SLO.
- Partner with Platform engineering team to design and implement CICD, GITOps, and IAC (Terraform) modules. Making sure we use our AgentOps NSA, standards, Ref. architecture and tooling.
- Partner with enterprise release management and AI Governance team to build \& deploy AI solutions using their platform tooling. Supporting entire AI lifecycle as per the standard work template.
- Build standardized deployment templates, reference architecture, automation scripts, terraform modules, CICD pipelines, and operational runbooks for AI workloads.
- Design and build IDP (Harness) catalogs, templates \& pipelines partnering with enterprise platform engineering team.
- Manage production systems to ensure our enterprise SLOs are met.
- Manage incident response for production systems, including triaging, escalating, RCA and implementing corrective actions.
Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, or a technical field.
- 10\+ years building and shipping software and/or platform solutions for enterprises.
- Programming experience with Python is required.
- 3\+ years of experience with IAC (Terraform).
- 5\+ years of experience owning production CICD, GitOps and release management gating.
- 3\+ years of experience in implementing observability, performance \& reliability solutions: SLO, P99\-95 latency, alert tuning, \& dashboards.
- Experience with AI observability/monitoring tools such as Dynatrace, Splunk, Arize \& OpenTelemetry/OpenInference is must.
- Proven experience with Google's Gemini Enterprise Agent platform is a plus.
- Experience with GKE/Docker/Registry is a plus.
- Proven experience in working with other cloud providers such as AWS cloud is a plus.
- Experience with Automated Testing, Automated Deployments, Agile methodologies, Unit Testing, and Integration Testing tools.
- Conversational UX/UI design (multi\-turn chatbots) and Human\-Agent\-Interaction (HAI) is a plus.
- Experience with IR, vector embedding, and Hybrid/Semantic search technologies.
- Experience with LLM orchestration frameworks like Langchain, LlamaIndex, LangSmith, LangGraph, Google Agent Development Kit, is a plus.
- Experience with Generative AI Guardrails, responsible AI, adversarial attack mitigation, and red teaming is a plus.
- Foundational understanding of Natural Language Processing and Deep Learning.
- Excellent problem\-solving skills and the ability to work in a collaborative team environment.
- Excellent communication skills.
This role can have a Hybrid or Remote work arrangement. Candidates who live near one of our office locations (Hartford, CT, Charlotte, NC, Chicago, IL, Columbus, OH) 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.
Candidates 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 \- $220,000
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
Salary Context
This $168K-$220K range is above 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 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($194K) sits 7% above the category median. Disclosed range: $168K to $220K.
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.
The Hartford AI Hiring
The Hartford has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Positions span Columbus, OH, US, Charlotte, NC, US, Hartford, CT, US. Compensation range: $151K - $234K.
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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