Principal Applied AI Solutions Engineer, AI Lab

GE Appliances, a Haier companyLouisville, KY
Onsite

About The Position

The Principal Applied AI Solutions Engineer serves as a senior technical builder within the GE Appliances AI Lab. This role leads the hands-on engineering and experimentation required to turn emerging AI capabilities and complex business problems into working prototypes, reusable technical patterns, and scalable solution concepts. This individual maintains technical ownership from solution design through development, testing, evaluation, and handoff. The role works closely with Applied AI Solutions Partners, business subject-matter experts, Digital Technology, and the AI Platform team to ensure experiments are technically sound, responsibly developed, and structured for broader adoption when appropriate. This is a hands-on builder role with broader enterprise influence. Success is measured not only by what this role builds, but by how effectively it helps functions redesign work, adopt repeatable AI-enabled practices, and learn how to extend solutions over time. The GE Appliances AI Lab helps the company become a frontier firm by learning and experimenting faster than the market. The Lab tests emerging AI capabilities against real business problems, rapidly builds working prototypes, and converts what it learns into reusable technologies, methods, and capabilities that functions can adopt.

Requirements

  • Bachelor’s degree in a relevant field or equivalent practical experience.
  • 7+ years of experience in software engineering, data engineering, automation, analytics engineering, AI/ML, digital product development, digital transformation, or related applied problem-solving work.
  • Experience building prototypes or internal solutions using AI, automation, analytics, or data-driven technologies.
  • Experience with GCP and integrating workflows, systems, or data using APIs, scripts, automation tools, or similar methods.
  • Experience applying generative AI, LLMs, prompt engineering, RAG, embeddings, or AI-assisted application development in practical business settings.
  • Strong problem-solving skills, including the ability to work through ambiguous and complex cross-functional challenges.
  • Strong communication and influencing skills with the ability to explain technical concepts clearly to business users, subject matter experts, and leaders.
  • Curiosity, ownership, and the ability to learn new tools and methods quickly.

Nice To Haves

  • Strong hands-on experience with Python, SQL, cloud platforms, and modern prototyping tools
  • Experience with n8n, BigQuery, Vertex AI, Streamlit, Tableau, GitHub, or similar platforms.
  • Experience building workflow automations, internal productivity tools, knowledge tools, or decision-support solutions.
  • Experience building end-to-end agents.
  • Experience working across manufacturing, supply chain, commercial, service, engineering, finance, HR, or other enterprise functions.
  • Experience supporting workshops, training, hackathons, capability-building programs, or cross-functional change efforts.
  • Familiarity with testing, documentation, version control, and iterative release practices.
  • Understanding of responsible AI, privacy, cybersecurity, data governance, and enterprise software standards.

Responsibilities

  • Lead hands-on experimentation with AI tools, models, automation platforms, agents, and development methods.
  • Architect and build AI-enabled prototypes, agents, applications, workflows, data pipelines, and system integrations using Python, SQL, APIs, GCP, n8n, and other approved technologies.
  • Own the technical design and implementation of AI Lab experiments, from initial proof of concept through user testing, technical evaluation, documentation, and potential production handoff.
  • Find and experiment with AI frontier tools and technologies.
  • Translate ambiguous business needs into practical prototypes, workflows, automations, and lightweight applications.
  • Partner across functions to identify high-value AI opportunities and shape solutions that improve productivity, quality, knowledge access, decision making, or employee experience.
  • Guide testing, feedback, iteration, and evaluation of prototypes, including what should be reused, refined, or scaled.
  • Create reusable playbooks, patterns, examples, and learning assets that can be adapted across teams and functions.
  • Coach teams on use case identification, workflow redesign, prompting, output validation, and practical AI adoption.
  • Work closely with Digital Technology and the AI Platform team when experiments require data access, authentication, enterprise integrations, model services, deployment support, monitoring, or a production pathway.
  • Determine, with DT and the AI Platform team, when a prototype should remain an experiment, be converted into a reusable enterprise pattern, or transition to production engineering.
  • Help define and track business impact, adoption, capability growth, and responsible AI practices across AI Lab initiatives.

Benefits

  • Flexible work arrangement
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