Senior AI Machine Learning Engineer

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.

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.

• 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.

• Bachelor’s degree in related field or 6+ years of equivalent experience in software engineering, data engineering, ML/Dev Ops engineering, applied AI engineering, or closely…