Principal, Applied AI Engineer

Bridgestone Americas•Nashville, TN

18h

About The Position

The Principal Applied AI Engineer leads the engineering of AI, generative AI (GenAI), and agent solutions built by Bridgestone West’s Business Insights & Advanced Analytics team for its Americas and Europe, Middle East and Africa business. This is a technical leadership role without direct reports and has no research component. The role sets engineering standards, approves designs, provides engineering sign-off for go-live, and defines approaches where no established pattern exists. The position builds on AI already in production, including Bridgestone GPT, the internal GenAI assistant, and Genie agents built on Databricks. It develops agents for multi-step business processes and reusable components to help business units adopt AI across product development, manufacturing, supply chain, sales, retail, fleet, and corporate functions. The role reports to the Director, Business Insights & Advanced Analytics and works with business stakeholders, the enterprise data platform team, and engineers from the Global Capability Center and delivery partners.

Requirements

Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience.
At least 10 years of production software engineering experience, mostly in Python, including at least 5 years building and running production machine learning or AI solutions and at least 3 years working on GenAI, LLM, or agent systems.
At least 3 years of technical leadership at Lead or Staff level or equivalent, directing the technical work of other engineers, including globally distributed teams.
Record of setting engineering standards used by other engineers and shipping and maintaining production code on which other teams depend.
Strong grounding in machine learning fundamentals and GenAI application patterns, including retrieval-augmented generation with embeddings and vector search, and methods for measuring output quality.
Hands-on experience with agent frameworks and orchestration such as LangGraph, LangChain, or equivalent, including tool use, building and consuming MCP servers, memory, and human-in-the-loop controls; sound judgment about when agents are not appropriate.
Experience with release and operations practices for models and LLM applications, including automated evaluation suites, tracing, CI/CD, rollback, drift detection, and version-controlled and tested prompts.
Experience with GenAI security and cost control, including prompt injection defense, data leakage prevention, access control, and selecting designs within a budget.
Hands-on experience with Databricks, including Databricks Genie and Databricks AI tooling, on AWS or Microsoft Azure.
Experience designing APIs and integrating with enterprise systems, and working with governed data where classification and access controls determine what can be built.
Daily use of AI coding tools in engineering, with the judgment to review and test their output.
Ability to explain technical trade-offs in plain language to non-technical stakeholders, select simple solutions that meet needs, write actionable design decisions and risk assessments for senior leadership, and influence technical direction across teams and time zones without formal authority.

Nice To Haves

Experience with SAP source data.
Experience in manufacturing, supply chain, or another industrial domain.
Experience delivering AI under regulatory requirements, such as the EU AI Act.

Responsibilities

Set technical direction for AI engineering and advise the Director and senior leadership on technology choices.
Define engineering standards for code review, testing, versioned prompts and retrieval patterns, automated evaluation, releases and rollbacks, and safe use of AI coding tools.
Review and approve designs and code from Global Capability Center and partner engineers, and coach them to team standards; coach business intelligence and analytics teammates taking on AI and agent work.
Own the architecture, releases, and enhancements of production AI assistants and agents, including Bridgestone GPT and Genie agents.
Design, build, and maintain AI solution components, including vector search indexes and endpoints, Model Context Protocol servers, agents, AI workflows, and predictive models.
Ground solutions in governed data products and shared business definitions, and connect them to Bridgestone data and systems through approved APIs and platform services.
Run proofs of concept, provide feasibility assessments and effort estimates, and recommend whether to build, reuse, buy, or not use AI for each opportunity.
Assess new models, frameworks, and vendor tools, and recommend which should be added to approved platform services.
Share standards and reusable patterns with other Bridgestone AI teams and contribute to architecture and technology reviews across IT.
Build automated evaluation suites, tracing, and regression checks, and require them to pass before changes go live.
Implement GenAI security guardrails, including defenses against prompt injection, prevention of data leakage, and access controls on model and agent capabilities.
Set cost, response-time, and reliability targets within each use case’s budget and select models and designs to meet them.
Take solutions through AI governance, Legal, Privacy, and Security review, and provide or withhold engineering sign-off against release criteria covering evaluation results, documentation, and security controls.
Make solutions operable and measurable after launch through runbooks, monitoring, adoption reporting, business measures, and feedback loops; lead technical responses to production incidents and follow-up reviews.
Publish components, agent definitions, and architecture guidance as reusable assets, and require partner engineers to deliver code and documentation to Bridgestone repositories and hand over knowledge before engagements end.