AI Engineer Senior

Vertiv•Westerville, OH

4h•Onsite

About The Position

The AI Engineer Senior will Design, build, and operationalize full stack AI applications that power Vertiv’s Enterprise AI Platform and other AI initiatives; and contribute to building and deploying AI services, retrieval pipelines, and agent-based applications integrated with enterprise systems. This role focuses on developing scalable AI solutions while contributing to enterprise standards and best practices. This position will be based onsite at Vertiv's Westerville, OH - HQ location.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent practical experience.
3+ years of professional full stack software development experience.
2+ years of hands on experience delivering production GenAI or LLM based applications.
Strong experience with Python and SQL; experience building APIs and data driven services.
Practical knowledge of LLMs, RAG architectures, prompt engineering, and vector search.
Experience deploying services in cloud environments (AWS or equivalent) using containers and CI/CD pipelines.
Ability to translate business requirements into scalable technical solutions with some guidance.

Nice To Haves

Experience with Snowflake for analytics, data access, or ML adjacent workloads.
Experience building internal AI tools or lightweight web apps for business users.
Familiarity with multi agent workflows and enterprise AI platform patterns such as AWS Quick Suite.
Experience with AI evaluation, monitoring, and LLMOps tooling.

Responsibilities

Develop AI applications across the stack, including web interfaces, backend APIs, and AI services deployed on AWS.
Build and maintain Retrieval Augmented Generation (RAG) pipelines, including ingestion, embeddings, and retrieval.
Develop reusable AI services, agents, and APIs aligned with established standards.
Implement AI capabilities and support adoption by application teams.
Integrate approved large language models through governed APIs with focus on performance and reliability.
Connect AI applications to enterprise data sources such as Snowflake.
Work with secure APIs and follow enterprise governance and compliance requirements.
Contribute to CI/CD pipelines and deployment of AI services.
Implement monitoring and evaluation practices to improve AI solution performance.
Collaborate with stakeholders to deliver AI use cases.
Document designs and contribute to shared components.