principal engineer-AI Sourcing & Procurement

Starbucks•Nashville, TN

5d•Hybrid

About The Position

Now Brewing – Engineering Principal, AI Sourcing & Procurement Platforms (Nashville, TN) Location: Nashville, TN (Onsite – four days per week) From the beginning, Starbucks set out to be a different kind of company—one that not only celebrated coffee and its rich tradition, but also built meaningful connections. We are known for developing extraordinary leaders who are guided by service to others and driven to make an impact at global scale. We are seeking a hands‑on Engineering leader to head the development of AI‑powered sourcing and procurement platforms, modernizing how Starbucks plans, sources, negotiates, and executes across a complex global supply chain. This leader will build and scale secure, reliable, and high‑velocity AI systems that directly impact cost, availability, and operational resilience. This role is based in Nashville and sits at the intersection of AI engineering, enterprise platforms, and real‑world business execution, with an expectation of rapid iteration, strong technical rigor, and close partnership with procurement, supply chain, and business teams

Requirements

Bachelor’s degree in computer science or information systems or equivalent experience.
Minimum 10 years of technology related work experience
Minimum 2 years leveraging LLMs in development
Must love to code and work through engineering challenges using GenAI
8+ years of building scalable services on top of public cloud infrastructure, preferably Azure and AWS
8+ years’ experience designing, building and operating large-scale distributed systems and infrastructure
5+ years’ experience with data and AI platforms (e.g. Databricks, Azure)
Deep knowledge of containerization & orchestration (Kubernetes, Docker), IaC and CI/CD technologies.
Experience working with AI and Machine Learning frameworks (e.g. LangChain, LangGraph, Semantic Kernel, TensorFlow, PyTorch), and APIs
Proficiency with at +1 scripting language (e.g. Python, Powershell, Go)
Proficiency in RAG pipelines, experience with multi-agent orchestration (MCP, A2A, etc), and skill/tool use is critical.
Ability to identify, analyze and resolve complex technical issues, ensuring optimal performance, scalability and user experience.
Strong communication and collaboration skills with cross-functional partners, including those with and without technical backgrounds.
Demonstrated willingness to learn continuously, adopt new technologies and approaches, and share knowledge within the technical community.
Growth-minded, solution-oriented approach with a proven track record of driving projects from concept to impact.
Experience in managing geographically distributed teams.

Responsibilities

Define and drive the technology vision and roadmap for AI‑enabled sourcing and procurement platforms, balancing speed, scalability, security, and reliability.
Partner closely with procurement, supply chain, finance, product, and data science teams to translate business needs into AI‑driven capabilities (e.g., sourcing optimization, supplier intelligence, contract insights).
Lead design decisions for agentic and LLM‑powered systems, ensuring platforms can evolve safely and predictably as models, tooling, and use cases change.
Operate effectively in a dynamic, fast‑moving environment, with wicked‑fast deployment cycles and a bias toward responsible delivery over perfection.
Lead and mentor a team of platform, infrastructure, and AI engineers, fostering a culture of ownership, learning, and execution.
Set clear technical standards and expectations while empowering engineers to move quickly and safely.
Coach partners through ambiguity, trade‑offs, and real‑world constraints common in applied enterprise AI.
Enable high developer productivity through CI/CD, infrastructure‑as‑code, Kubernetes, and automated testing, with a strong emphasis on deployment speed and reliability.
Establish and use operational metrics (DORA metrics, SLOs, SLIs, error budgets) to balance innovation with stability.
Implement best‑in‑class monitoring, logging, and alerting to ensure platform health and rapid incident response.
Understand model tradeoffs, token management and.
Ensure back testing, AI evaluations, and performance benchmarking are integral to model and agent development—not optional afterthoughts.
Embed AI security, data protection, and access controls by design, partnering closely with Security and Architecture teams.
Drive disciplined practices around prompt management, model versioning, regression testing, and controlled rollout of AI capabilities.
Ensure AI systems are explainable, auditable, and appropriate for enterprise sourcing and procurement use cases.
Hands‑on familiarity with modern AI development tools, including GitHub Copilot, Cursor, and similar AI‑assisted engineering tools, and an expectation to model their effective use.
Build and operate systems leveraging LLMs, agentic orchestration, and intelligent workflows aligned to real business outcomes.
Collaborate across data platforms and enterprise systems supporting procurement and supply chain operations.