Jobs at The Hartford Financial Services Group, Inc.

Build and maintain scalable cloud-native ETL/ELT pipelines and data platforms to support analytics and ML. Implement IaC and CI/CD, enable MLOps, ensure data quality, monitoring, and collaborate with data scientists and product teams to deploy production AI services.

Handle a caseload of first- and third-party Cyber & Technology claims from intake to resolution, including litigated matters. Investigate cyber incidents, determine coverage and liability, manage defense counsel and litigation strategy, set reserves, negotiate settlements, attend trials/mediations, and collaborate with stakeholders on trends, training, and product initiatives.

Design, build, and operate enterprise AI/ML platform capabilities (AIOps/MLOps/LLMOps). Develop GenAI features, experiment with models and services, and architect end-to-end solutions. Collaborate with data, cloud, and business teams, drive engineering execution, promote best practices, and mentor engineers. Ensure platform reliability, monitoring, and scalable deployments across cloud environments.

Lead and coach a team of Senior Client Relationship Managers serving national accounts. Drive growth, renewals, stewardship, cross-functional collaboration, escalation resolution, performance tracking, licensing compliance, and continuous improvement to deliver exceptional client experiences.

Design, build, and maintain scalable data assets and pipelines for analytics and ML; validate and ingest diverse data sources; perform exploratory data analysis and data quality checks; collaborate with cross-functional teams and produce reproducible code and documentation.

The Senior IT Engineer will lead full stack development projects focused on AWS, architecting APIs, mentoring junior engineers, and ensuring application performance and security.

Design and build production-scale data pipelines, orchestration, and integrations on GCP to enable metadata-driven governance, lineage, automation, and AI-enabled workflows. Implement secure, observable, cost-efficient cloud data solutions, embed governance into engineering workflows, and drive CI/CD, testing, monitoring, and platform engineering best practices.

Manage multinational network partner relationships and service delivery to meet KPIs. Lead stewardship meetings and in-person visits, handle escalations, support onboarding, maintain Global Online portal and mailbox, perform SLA reporting, assist with outbound reinsurance slips, premium reconciliation, and local policy quality control.

Handle a caseload of Architects & Engineers professional liability claims from inception to resolution, including litigated matters. Investigate, evaluate coverage/liability, set reserves, manage defense counsel and litigation budgets, negotiate settlements, attend trials/mediations, prepare reports, provide training and contract reviews, and collaborate with underwriters and business partners on risk management.

Lead alignment of Enterprise Data Services or Applied AI & Analytics portfolios with enterprise data/AI strategy. Oversee portfolio prioritization, IT budgeting, financial accountability, vendor management, and value-stream coordination. Partner with executive stakeholders to meet efficiency targets and manage annual portfolio spend (~$90M+). Promote Agile/DevOps/Scaled Agile practices and continuous improvement.

Design, build, and operationalize production-grade AI/Machine Learning solutions and MLOps services. Deploy models and API services to AWS and GCP using CI/CD and IaC, implement workflow automation, monitor post-production performance, research generative/ML techniques, and mentor junior engineers while collaborating with cross-functional data, cloud, and architecture teams.

Lead Applied AI strategy and execution across Employee Benefits and Operations, overseeing ML, generative and agentic AI, RAG, and predictive analytics lifecycles. Drive governance, model risk management, evaluation, deployment, cross-LOB prioritization, talent architecture, and regulatory readiness to improve enrollment, billing, service, and operational efficiency.

The AVP Applied AI leads data science and AI capabilities at The Hartford, managing complex AI portfolios and ensuring delivery of business value through scalable models and AI-driven systems. Responsibilities include overseeing team performance, technical direction, AI governance, and alignment with business outcomes, along with collaboration across departments and continuous improvement initiatives.

Lead planning, forecasting, and performance analysis for P&C lines; partner with finance, actuarial, and business leaders to provide insights, govern models, automate processes, and prepare executive reporting. Drive assumptions for loss ratios, catastrophes, reinsurance analyses, and earnings prep while mentoring a team and implementing analytics and AI-enabled improvements.

Architect and build production-grade agentic AI systems and platforms using Google ADK, MCP, LangChain/LangGraph, and AlloyDB. Develop agent harnesses, RAG/GraphRAG pipelines, vector retrieval and memory stores, Terraform-based GCP infrastructure, CI/CD for model deployments, and enterprise security and governance for sessioned agent interactions.

Lead design and delivery of scalable, secure software across full stack and cloud. Collaborate with product and architecture teams, mentor engineers, drive best practices, integrate AI-assisted tools, and participate in the full SDLC to ensure system stability and performance.

Lead infrastructure resilience for cloud and SaaS systems by implementing observability, IaC, CI/CD automation, incident triage and self-healing. Build tooling, alerts, AI-driven anomaly detection and runbooks, partner with architecture and infrastructure teams to improve reliability, performance, and security.

Lead development and maintenance of workers' compensation pricing methodologies and tools. Update model parameters, drive pricing initiatives, collaborate with underwriting/product/data science/IT, provide profitability analyses, and support implementation to improve accuracy, segmentation, and profitable growth across business segments.

Lead infrastructure resilience by implementing observability, IaC, automation, and AI-driven detection to ensure cloud and SaaS system stability. Drive tooling, alerts, self-healing, CI/CD optimization, incident triage/restore, and partner across architecture and engineering to improve performance, reliability, and cost efficiency.

Lead design and build of an enterprise AI platform enabling GenAI and ML solutions: define roadmap, deliver POCs, implement production-grade platform capabilities (IaC, monitoring, FinOps), collaborate with architecture/cloud/security, mentor engineers, and accelerate deployment and scaling of AI use cases.