Sr. GenAI Architect

Job Title\: Sr. GenAI Architect

Hyundai Capital America (HCA) helps people move forward. Through Hyundai Motor Finance, Genesis Finance, and Kia Finance, we deliver innovative financing, leasing, and insurance solutions to more than 3 million customers and businesses nationwide.

We’re a company driven by growth, innovation, and people. At HCA, you’ll find opportunities to build new skills, expand your career, and make a real impact—while working in a diverse, inclusive, and values‑driven environment. We’re proud to support our communities through volunteerism, philanthropy, and engaged Employee Resource Groups.

If you’re looking for a fast‑paced, collaborative workplace where your ideas matter, join us as we lead the future of financing freedom of movement. Apply today.

WORK MODEL

#LI-OnSite (4 days a week)

WHAT YOU WILL DO

The Sr. GenAI Architect will own enterprise-wide end‑to‑end architecture for GenAI products\: model selection, retrieval, agent orchestration, guardrails, observability, and reliability. This role will design and implement scalable GenAI solutions for business use cases and provide technical leadership and accelerate knowledge transfer.

HOW YOU WILL MAKE AN IMPACT

1. Define reference architecture (LLM selection, RAG, agent framework, APIs). Lead development of agentic and orchestrated multi-agent architectures. Establish coding standards, secure patterns, and SRE/observability baselines.

2. Partner with internal teams to build CI/CD, model registry, feature stores, vector database. Collaborate with engineers to integrate models with enterprise data pipelines for data/model/artifact versioning and automate deployments. Operate model/agent registries and governance hooks (approval gates, audits). Set up tracing, logging, eval integration, and cost dashboards. Architect scalable pipelines, including model training, deployment, monitoring, and CI/CD workflows

3. Deploy AI models into production environments and ensure that models are integrated into existing systems and can handle real-time data inputs effectively. Troubleshoot issues that arise during model deployment and operation to ensure solutions are implemented quickly to minimum downtime. Continually monitor performance to detect any issues, such as drops in accuracy or changes in data patterns. Implement monitoring systems to ensure models remain effective over time.

4. Lead design reviews and technical decisions to ensure AI solutions meet business objectives, performance standards, and ROI goals.