Role overview
Opportunity Information:
Worldpac, a private equity-owned leader in automotive parts distribution, is seeking an AI Engineer to identify, architect, and deploy production GenAI applications across the enterprise. In this role you will partner with teams in Finance, Inventory, Procurement, Operations, etc. to discover high-value AI opportunities, then build and productionize solutions that deliver measurable ROI.
You'll combine strategic discovery with hands-on engineering and production deployment. Working in a modern Azure Databricks and Snowflake environment, you'll ship LLM-powered agents and automation that transform business operations and have true impact.
What you'll work on
- Identify and prioritize GenAI use cases across departments through stakeholder partnerships and operational deep-dives.
- Architect and develop prototype agents using private ChatGPT instances, Azure OpenAI, Anthropic Claude, and open-source LLMs
- Implement RAG systems, multi-agent orchestration, and intelligent automation using frameworks such as LangChain, LlamaIndex, or LangGraph.
- Build and deploy automated PoCs that demonstrate feasibility, value, and integration potential with enterprise systems.
- Evaluate and tune prompt engineering strategies, tool integrations, and memory handling for agent reliability and accuracy.
- Build API services (FastAPI) integrating with enterprise systems (AS400/IBM i, Salesforce, Oracle, etc.) and Snowflake dataStay on top of GenAI research, LLM fine-tuning techniques, and agentic design patterns to continually evolve our internal capabilities.
- Collaborate with Data Engineers and Software Engineers to transition PoCs into robust, secure, and scalable enterprise applications.Create documentation, reusable components, and establish GenAI engineering standards to accelerate AI adoption across the company.
- Deploy applications on Azure infrastructure with CI/CD pipelines, MLOps workflows, monitoring, and cost optimization
What we're looking for
- Experience working within or supporting large industrial or B2B distribution businesses.
- Familiarity with DevOps or MLOps workflows for AI model deployment.
- Exposure to data privacy, governance, and secure model deployment in regulated enterprise environments.
- Prior work with open-source or commercial RAG systems, embedding models, or vector search (e.g., FAISS, Weaviate, Pinecone).
- Ability to mentor other developers or analysts on GenAI development best practices.
Location Type: On-Site Pay Range: USD $170,000.00 - USD $200,000.00 /Yr.
Tags & focus areas
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