Senior AI Machine Learning Engineer

The Hartford

. Lead day-to-day engineering execution for the Employee Benefits predictive model portfolio, including pricing and underwriting models, scoring pipelines, model refreshes, monitoring, data validations, and production support .

Posted 9/21/2026full-timeUnited StatesSenior💰 $117,200 - $175,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and maintaining AI/ML components and data pipelines, with a strong focus on production-grade code delivery and operational excellence in cloud environments. Proficient in leading engineering teams, guiding junior engineers, and collaborating with cross-functional stakeholders to ensure effective model governance and deployment.

Highest-signal resume keywords

Python ProgrammingAWS Cloud ServicesGCP Cloud ServicesMachine Learning EngineeringCI/CD Practices

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

SQLSDLC PracticesAutomated TestingProduction-Grade Code DeliveryFeature PipelinesModel Training WorkflowsBatch ScoringInference ServicesModel MonitoringDrift Detection

Soft Skills

LeadershipCommunicationMentoringProblem-SolvingCollaboration

Tools & Technologies

AirflowCloud ComposerStep FunctionsVertex AI PipelinesAPIsInfrastructure-as-Code

Industry Keywords

InsuranceEmployee BenefitsPricingUnderwritingRisk SelectionSales EnablementPolicy Lifecycle Analytics

Tech Stack

Tools & technologies

AirflowAWSCloudGoogle Cloud PlatformPythonSDLCSQL

About the role

Key responsibilities & impact

Lead day-to-day engineering execution for the Employee Benefits predictive model portfolio, including pricing and underwriting models, scoring pipelines, model refreshes, monitoring, data validations, and production support
Build, deploy, and maintain AI/ML components and data pipelines for pricing, underwriting, sales, service, renewal, and policy lifecycle workflows
Implement approved solution designs and translate design patterns into tested, reliable production code and workflows
Support generative AI and agentic AI solutions, including prompt orchestration, retrieval-augmented generation patterns, evaluation workflows, guardrails, and ecosystem integration
Develop and operate batch and near-real-time data/AI pipelines for model training, feature generation, inference, post-processing, business rules integration, and downstream consumption
Deploy and sustain production AI services, jobs, APIs, and workflows in AWS and GCP using approved CI/CD, testing, observability, security, and operational practices
Own implementation quality through code reviews, unit/integration testing, documentation, runbooks, production readiness checks, and incident response support
Guide and mentor junior engineers
Partner with Data Scientists, Data Engineers, Asset Owners, Underwriting, and Pricing stakeholders to understand requirements, validate outputs, resolve data issues, and fit solutions to business workflows
Maintain model and pipeline governance artifacts, including lineage, model inputs/outputs, monitoring metrics, validation evidence, operational controls, and handoff documentation
Identify risks, bottlenecks, and operational gaps and recommend practical improvements

Requirements

What you’ll need

Bachelor’s degree in related field or 6+ years of equivalent experience in software engineering, data engineering, ML/DevOps engineering, applied AI engineering, or closely related technical roles
Strong hands-on expertise in Python, SQL, SDLC practices, Git-based development, automated testing, and production-grade code delivery
Experience deploying and operating data, AI, or ML workloads in AWS and GCP, including cloud storage, managed compute, orchestration, IAM-aware access patterns, logging, and monitoring
Experience with ML engineering concepts such as feature pipelines, model training workflows, batch scoring, inference services, model monitoring, drift detection, validation, retraining, and production support
Ability to work within defined architecture, enterprise security standards, data governance expectations, coding standards, and operational controls
Ability to lead implementation work, guide junior engineers, communicate tradeoffs, and manage multiple model/pipeline deliverables with limited day-to-day direction
Candidates must be authorized to work in the US without company sponsorship
Master's degree in computer science, engineering, information technology, MIS, data science, or related discipline preferred
Experience in insurance, employee benefits, pricing, underwriting, risk selection, sales enablement, or policy lifecycle analytics
Experience supporting predictive model portfolios requiring periodic refreshes, performance tracking, business validation, and governed production deployment
Experience with generative AI or agentic AI implementation patterns, including RAG, prompt evaluation, LLM application integration, AI safety controls, human-in-the-loop workflows, and model output validation
Experience with orchestration and workflow tools such as Airflow, Cloud Composer, Step Functions, Vertex AI Pipelines, or comparable enterprise platforms
Experience with CI/CD, containers, APIs, infrastructure-as-code concepts, observability, and production incident management

Benefits

Comp & perks

Short-term or annual bonuses
Long-term incentives
On-the-spot recognition
Hybrid work schedule