AI Engineer

Job summary

Description: Job Summary Spr*** is a leader in the custom window treatment industry since 1939. Headquartered in Middleton WI, we have 6,000 associates with locations in North America, Europe, and Asia.

Qualifications

• As North America’s premier window covering company, we’re committed to creating a “Best Experience” for our consumers, channel partners and associates.
• Required Education/Experience
• 5+ years building real software in engineering, machine learning, data engineering, or AI development
• Hands-on experience shipping machine learning and generative AI solutions into production, not pilots that stalled in a notebook
• Fluent in Python and modern AI/ML frameworks such as TensorFlow, PyTorch, LangChain, or Hugging Face
• Comfortable building on cloud platforms such as Microsoft Azure, AWS, or Google Cloud
• Experience wiring AI solutions into real enterprise systems and APIs
• Solid grasp of AI governance, model lifecycle management, and security best practices
• Sharp analytical and problem-solving instincts, and the ability to explain your work to an executive in two sentences and to an engineer in two hundred
• Comfortable delivering in fast Agile cycles, iterating in the open rather than waiting for perfect.
• Preferred Experience
• Experience with Microsoft Copilot, Azure OpenAI, or enterprise generative AI platforms.

Responsibilities

• Write, test, and ship real AI and machine learning code that makes it into production, with support from senior engineers.
• Build features for generative AI applications—LLMs, RAG, copilots, and automation—that real people across the business actually use.
• Help translate business problems into working prototypes and features, learning the domain and the trade-offs as you go.
• Build and maintain AI pipelines, APIs, and integrations, then improve them based on real feedback.
• Collaborate with data engineering teams to ensure high-quality, governed, and accessible data for AI initiatives.
• Develop AI-enabled analytics and predictive models supporting manufacturing, supply chain, customer service, sales, and operations.
• Follow AI governance, security, and responsible-AI practices, and help monitor models running in production.
• Help optimize model performance, scalability, reliability, and operational efficiency.
• Explore new AI

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