Lead AI Engineer - AI Platform

Lowe's Companies, Inc.•Charlotte, NC

2d•Onsite

About The Position

The primary purpose of this role is to lead artificial intelligence (AI) engineering initiatives and the development of AI solutions that enable the integration and evolution of advanced machine learning (ML) models, including deep learning (DL) frameworks and large language models (LLMs). The Lead AI Engineer will be responsible for leading the advancement of model performance, implementing essential tools and frameworks, and optimizing compute and storage solutions to elevate AI technologies to new heights.

Requirements

Bachelor’s degree in science, technology, engineering, math, or related field or equivalent years of experience in lieu of education requirement, if applicable
6 years of experience executing and deploying data science, ML, and DL (fewer years may be accepted with a Master's or Doctorate degree), ML platform engineering, MLOps tools, and ML frameworks
6 years of programming experience in Java and/or Python
4 years of experience working with cross-functional partners

Nice To Haves

Master’s degree in science, technology, engineering, math, statistics, physics, economics, data science, information science, or quantitative analytics
5 years of experience in SQL and NoSQL databases, Hadoop ecosystem, Druid, Trino, Big Query, Google Vertex AI
6 years of Python programming experience.
A minimum of 3 years of experience in AI technology outside of educational training preferred.

Responsibilities

Leads the development and evolution of the AI platform, ensuring it is scalable, reliable, and capable of supporting a broad range of AI models and emerging AI capabilities through reusable APIs.
Leads the architecture, design, development, and implementation of tools and frameworks that accelerate ML experimentation, model deployment, and production-grade AI capabilities.
Architects and manages GPU and CPU compute resources to optimize model performance, cost efficiency, capacity planning, and resource utilization.
Mentors and guides engineers, data scientists, and AI specialists to enable seamless, secure integration of AI models into the platform.
Leads the design and operation of robust data movement strategies and pipelines that support high-volume model training, evaluation, inference, and feedback loops.
Drives continuous platform improvement through performance analytics, reliability metrics, user feedback, and measurable adoption outcomes.
Leads cross-functional technical initiatives, combining deep AI engineering expertise with strategic business context and user needs.
Champions security, governance, and responsible AI practices, ensuring data integrity, access controls, compliance, model risk management, and alignment with enterprise standards.
Defines scalable AI/ML platform architecture, including model integration patterns, reusable APIs and frameworks, enterprise design standards, and reference implementations.
Leads LLM and agentic AI engineering practices, including RAG, tool and MCP integration, agent orchestration, structured outputs, context engineering, guardrails, evaluation, and production observability.
Establishes MLOps and LLMOps standards for experimentation, deployment, model and prompt versioning, monitoring, drift and quality evaluation, CI/CD, incident response, and rollback.
Raises engineering quality through specification-driven development, automated testing, observability, architecture and code reviews, documentation, and rigorous validation of AI-generated outputs.
Provides technical leadership through architecture reviews, engineering mentorship, cross-functional influence, roadmap shaping, and translation of business problems into measurable AI platform outcomes.