Principal AI Product Engineer

Premier Inc.

4d•$141,000 - $234,000•Remote

About The Position

Principal AI Product Engineer What you will be doing: You will design and implement scalable AI capabilities across our product portfolio, turning advanced models, agents, and conversational interfaces into reusable components that can be embedded across multiple software solutions. Working closely with product, engineering, and data platform teams, you will establish integration patterns for technologies such as conversational AI, RAG, vector search, and model orchestration while leveraging the Databricks platform to build enterprise-ready AI infrastructure. You will also accelerate the delivery of AI-powered features by standardizing infrastructure, creating shared services, and defining governance, monitoring, and evaluation frameworks that ensure AI systems are reliable, secure, and production-ready.

Requirements

Years of Applicable Experience - 10 or more years
Bachelors (Required)
8+ years engineering experience, including distributed systems
Deep experience with Databricks platform architecture
Hands-on experience implementing Genie
Experience designing RAG pipelines and model-serving architectures
Strong backend engineering expertise (Python required)
Experience designing shared services or internal platforms
Strong understanding of governance, lineage, model evaluation, and AI reliability
Education Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related technical field required
Remain in a stationary position for prolonged periods of time
Be adaptive and change priorities quickly; meet deadlines
Attention to detail
Operate computer programs and software
Ability to communicate effectively with audiences in person and in electronic formats.
Day-to-day contact with others (co-workers and/or the public)
Making independent decisions
Ability to work in a collaborative business environment in close quarters with peers and varying interruptions

Nice To Haves

Advanced AI/ML engineering, including large language models (LLMs), RAG architectures, agent frameworks, and conversational AI systems
Expertise in the Databricks ecosystem (Unity Catalog, Delta Lake, Workflows, Model Serving, Genie)
Strong backend engineering in Python, including APIs, microservices, and distributed systems design
Experience building and deploying production AI systems, model serving pipelines, and scalable inference architectures
Proficiency with vector databases, embeddings, semantic search, and retrieval frameworks
Full-stack development experience including React, Next.js, TypeScript, and modern frontend frameworks for building AI-driven user interfaces
Experience designing API-first architectures, REST/GraphQL services, and AI-enabled application layers
Experience building shared platforms, SDKs, and internal developer tooling
Strong understanding of cloud-native architectures (AWS, Azure, or GCP).
Knowledge of data engineering and pipeline patterns, including ETL/ELT workflows and large-scale data processing
Experience with AI governance, model evaluation, monitoring, and observability frameworks
Strong understanding of performance, cost, and latency optimization for AI-powered applications
Ability to collaborate across product, data, engineering, and executive leadership to translate AI capabilities into scalable product solutions.
Advanced degree (MS or PhD) in AI, Machine Learning, Data Science, or a related discipline preferred
Equivalent practical experience building and deploying AI-enabled production systems may be considered in place of formal education
Ongoing engagement with emerging AI technologies, research, and modern engineering practices strongly preferred

Responsibilities

Productize AI Capabilities Turn models, agents, and conversational interfaces into reusable product capabilities that can be embedded across multiple solutions.
Define AI Integration Patterns Create scalable patterns for: Genie-powered conversational interfaces RAG and vector search integration Model orchestration and evaluation Ensure AI becomes a consistent, trusted layer across products.
Accelerate AI Deployment Reduce time-to-production for AI features by: Standardizing infrastructure Creating shared services and SDKs Defining guardrails and evaluation frameworks Enabling faster experimentation without sacrificing governance
Architect for Multi-Product Scale Work deeply in Databricks (Unity Catalog, Delta, Workflows, Model Serving, Genie) to create enterprise-ready AI foundations.