Senior AI Machine Learning Engineer

thehartford; Charlotte (NC); On-site; USD 140,000 - 170,000; Full time

Job summary

The Hartford is seeking a Senior AI Machine Learning Engineer within Employee Benefits Applied AI and Analytics to build, deploy, and sustain enterprise-scale predictive AI solutions across EB workflows. You will lead hands-on engineering, deploy production AI services in AWS and GCP, and mentor junior engineers while aligning models with business needs.

Ideal candidates have strong Python/SQL, SDLC, and experience with CI/CD, monitoring, and governance for enterprise data pipelines and

Qualifications

• Bachelor's degree or equivalent experience in software/data engineering or ML.
• 6+ years in software/data engineering, ML/DevOps or related roles.
• Hands-on Python, SQL, SDLC practices, and production-grade code delivery.

Responsibilities

• Lead engineering for enterprise-scale AI/ML assets in EB pricing and underwriting.
• Build and operate AI/ML data pipelines and production services in AWS/GCP.
• Guide junior engineers; ensure architectures meet security and governance.
• Support generative AI initiatives, prompt orchestration, and validation workflows.
• Collaborate with Data Scientists and stakeholders to align models with business needs.

Skills

Python

SQL

Git

CI/CD

Automated Testing

Production Code

Education

Bachelor's degree

Master's degree

Tools

AWS

GCP

Airflow

Job description

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 Hartford is seeking a Senior AI Machine Learning Engineer within Employee Benefits Applied AI and Analytics (EB AIA) to help build, deploy, and sustain enterprise-scale predictive and applied AI solutions across pricing, underwriting, sales related EB business workflows. As a Senior AI/ML engineer you will manage and modernize the existing predictive model portfolio while helping the team expand into generative AI, agentic AI and other applied AI capabilities.

The role is intended for a hands-on technical lead who can execute approved solution designs, deploy production-ready AI and ML components, operate reliable model pipelines, and guide junior engineers. The person should be able to translate architecture and design direction into working, governed , and production assets with minimal supervision.

Team Description

The Employee Benefits Applied AI and Analytics team provides insight, automation, and augmentation across the policy lifecycle for Employee Benefits customers and internal business stakeholders. EB AIA supports a portfolio that spans sales, pricing, underwriting, policy installation, renewal, service, and operational workflows.

In addition to the existing portfolio of Predictive AI assets, the team is scaling an end-to-end AI-driven reimagination of EB underwriting and service organizations. The team partners closely with enterprise platform enablement team to apply consistent architecture and engineering practices while tailoring solutions for accuracy, transparency, scalability, and business usability.

Primary Responsibilities

• Lead day-to-day engineering execution for the EB 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 that support applied AI use cases across pricing, underwriting, sales, service, renewal, and policy lifecycle workflows.
• Implement approved solution designs from senior Applied AI Engineers, Architects, and Data Scientists; translate design patterns into tested, reliable production code and workflows.
• Support the initial build-out of generative AI and agentic AI solutions, including prompt orchestration, retrieval-augmented generation patterns, evaluation workflows, guardrails, and integration with existing EB data and application ecosystems.
• 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 environments using approved CI/CD, testing, observability, security, and operational practices.
• Own implementation quality for assigned components, including code reviews, unit/integration testing, documentation, runbooks, production readiness checks, and incident response support.
• Guide and mentor junior engineers by breaking down technical work, reviewing code, explaining model/data pipeline patterns, and ensuring consistent engineering practices.
• Partner with Data Scientists, Data Engineers, Asset Owners, Underwriting, Pricing stakeholders to understand requirements, validate outputs, resolve data issues, and ensure model solutions fit 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 in deployed AI/ML solutions and recommend practical improvements under the guidance of senior technical leadership.

Minimum Requirements

• Bachelor's degree in related field or 6 + years of equivalent experience in s oftware engineering, data engineering, ML / DevOps engineering, applied AI engineering, or closely related technical roles.
• Master's degree in computer science, engineering, information technology, MIS, data science, or related discipline preferred.
• 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