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The Hartfordis seeking aSenior AI Machine Learning Engineerwithin Employee Benefits Applied AI and Analytics (EB AIA) to help build, deploy, and sustain enterprise-scale predictive and applied AI solutions across pricing, underwriting, salesrelated EB business workflows.As a Senior AI/ML engineer youwill manage and modernize the existing predictive model portfolio while helping the team expand into generative AI, agentic AIandother 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 productionassets withminimalsupervision.

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 enterpriseplatformenablementteamto 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,AssetOwners, Underwriting, Pricingstakeholders 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.

MinimumRequirements

Bachelor'sdegree in related field or6+ years of equivalent experience insoftware engineering, data engineering, ML/DevOpsengineering, applied AI engineering, or closely related technical roles.

Master'sdegree in computer science, engineering, information technology, MIS, data science, or related disciplinepreferred.

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.

Preferred Experience

Experience in insurance, employee benefits, pricing, underwriting, risk selection, sales enablement, or policy lifecycle analytics.

Experience supporting predictive model portfolios that require 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.

Success Profile

A successful candidate will be a hands-on engineering lead who can take a generated or approved architecture, convert it into deployable assets, keep predictive AI models running reliably, and help the team move into applied AI delivery. The candidate should be comfortable doing implementation work across pricing and underwriting under senior supervision, while also raising the capability of junior engineers through practical technical guidance.

This role will have a Hybrid work schedule, with the expectation of working in an office 3 days a week

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.