Be Here. Be Great. Working for a leader in the insurance industry means opportunity for you. Great American Insurance Group's member companies are subsidiaries of American Financial Group. We combine a "small company" culture where your ideas will be heard with "big company" expertise to help you succeed. With over 30 specialty and property and casualty operations, there are always opportunities here to learn and grow.
At Great American, we value and recognize the benefits derived when people with different backgrounds and experiences work together to achieve business results. Our goal is to create a workplace where all employees feel included, empowered, and enabled to perform at their best.
Responsibilities:
Strategic Leadership & Team Development
Define and drive the architectural vision for AI/ML Ops capabilities supporting Business Data & Analytics (BD&A) and the AI Hub, ensuring alignment with enterprise technology strategy and business priorities.
Lead, mentor, and develop a team of architects, developers, and external consultants/contractors (5–9 people), fostering a culture of technical excellence, innovation, and continuous improvement.
Manage vendor and consultancy relationships, including scoping engagements, overseeing deliverables, and ensuring external contributors integrate effectively with internal teams and standards.
Build and mature the team's capabilities in application development/automation, cloud engineering, and cloud security — closing critical capacity gaps and reducing key-person risk in these domains.
Establish a technology roadmap for the AI/ML Ops platform, balancing near-term delivery needs with long-term architectural sustainability and scalability.
Architecture, Engineering & Delivery
Design and build new solutions and accelerators across the enterprise data solution landscape, enabling faster, more reliable deployment of AI/ML workloads, data pipelines, and analytics products.
Architect and operationalize CI/CD pipelines in GitHub for BD&A and AI Hub deployments, establishing standards, templates, and reusable patterns that drive consistency and velocity.
Develop cloud architecture patterns and security guardrails for Snowflake (including Cortex and native LLM integrations) and Azure-native services, ensuring solutions meet enterprise security and compliance requirements.
Mature the ML Ops lifecycle — including model deployment, monitoring, versioning, and retraining pipelines — on the enterprise managed Kubernetes platform (CHEDDAR).
Drive deployment automation into the shared services Data team's rollout processes, reducing manual effort, improving reliability, and accelerating time-to-value.
Evaluate, prototype, and recommend emerging technologies and approaches that strengthen the organization's AI/ML and data platform capabilities.
Design and deliver production-grade Generative AI (GenAI) applications, including document intelligence solutions, retrieval-augmented generation (RAG) knowledge systems, and LLM-powered extraction and summarization services.
Own post-deployment operations, including hypercare, user feedback incorporation, and production issue resolution for AI and data-driven applications.
Cross-Functional Partnership
Partner with Cloud Engineering to ensure base Azure services are architected, configured, and optimized to support AI/ML and data workloads.
Collaborate with the Enterprise Information Security Group (EISG) to embed security and data controls into solution designs from the outset, rather than as an afterthought.
Work alongside the Gas Lab (front-end development) and the AI/Automation Hub (emerging technologies) to ensure end-to-end solution coherence from data platform through user experience.
Serve as a key technical liaison across stakeholder groups who build in R, Python, SQL, and Informatica IDMC, ensuring the platforms and tooling this team delivers meet their needs effectively.
First-Year Impact Milestones
- Stand up formalized CI/CD pipeline standards for BD&A and AI Hub deployments in GitHub.
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Establish cloud architecture patterns and security guardrails for Snowflake Cortex and Azure-based AI/ML workloads.
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Build and operationalize accelerators that measurably reduce time-to-deployment for the shared services Data team.
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Mature the ML Ops lifecycle with production-grade model deployment, monitoring, and retraining pipelines on CHEDDAR.
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Hire, onboard, and develop the team to full operational capacity.
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Establish a clear partnership operating model with Cloud Engineering, EISG, Gas Lab, and AI/Automation Hub — defining intake, collaboration norms, and escalation paths.
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Deliver a forward-looking technology roadmap with prioritized initiatives and a phased execution plan.
Required Qualifications:
10+ years of experience in solution engineering, application architecture, or related technology disciplines.
5+ years of experience designing and delivering solutions in public cloud ecosystems (Azure strongly preferred).
5+ years of experience in DevOps, platform engineering, or establishing technology platforms, including CI/CD pipeline design, infrastructure-as-code, and deployment automation.
Demonstrated leadership experience managing technical teams, including hiring, mentoring, and developing architects and engineers.
Strong expertise in application development and automation, with the ability to set standards, design patterns, and drive engineering quality across a team.
Working knowledge of cloud security principles — including identity and access management, network security, data protection, and compliance frameworks — sufficient to partner effectively with security teams and embed controls into solution designs.
Experience managing external vendors, consultants, or contractors, including scoping work, managing deliverables, and integrating external resources with internal teams.
Proven ability to operate at both strategic and tactical levels — comfortable setting architectural vision and roadmaps while also rolling up sleeves to design, build, and troubleshoot complex solutions.
Excellent communication and stakeholder management skills, with the ability to translate complex technical concepts for business audiences and influence across organizational boundaries.
Preferred Qualifications:
Experience with Snowflake ecosystems, including newer capabilities such as Cortex and native LLM integrations.
Familiarity with AI/ML Ops practices — model lifecycle management, experiment tracking, model monitoring, and automated retraining.
Experience with GitHub Actions or similar CI/CD platforms for pipeline automation.
Exposure to data engineering tools and languages commonly used in analytics organizations, such as R, Python, SQL, or Informatica IDMC.
Experience in insurance, financial services, or other regulated industries.
Familiarity with infrastructure-as-code tools (e.g., Terraform, Ansible) and GitOps practices.
Experience with Azure AI Foundry (or similar AI platform tooling) for model cataloging, lifecycle management, and deployment orchestration.
Experience deploying applications on managed container or platform-as-a-service environments
Business Unit:
Property & Casualty IT Services
Benefits:
We offer competitive benefits packages for full-time and part-time employees*. Full-time employees have access to medical, dental, and vision coverage, wellness plans, parental leave, adoption assistance, and tuition reimbursement. Full-time and eligible part-time employees also enjoy Paid Time Off and paid holidays, a 401(k) plan with company match, an employee stock purchase plan, and commuter benefits.
Compensation varies by role, level, and location and is influenced by skills, experience, and business needs. Your recruiter will provide details about benefits and specific compensation ranges during the hiring process. Learn more at http://www.gaig.com/careers.
Excludes seasonal employees and interns.
