Job Summary 

Springs Window Fashions 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.  Our custom window treatments are available under the Bali, Graber, SunSetter and Mecho brands in nearly every major retailer, in thousands of designer showrooms, and showcased in large commercial buildings.

Our company has made significant investments to become a leader in product innovation.  As North America’s premier window covering company, we’re committed to creating a “Best Experience” for our consumers, channel partners and associates.  We are bringing new innovations to the market at an accelerated pace and have a variety of offerings to consumers who want to improve their home décor.

This is an engineering role, not a research role. The AI Engineer is a hands-on builder who designs, ships, and operationalizes AI solutions that go live across the organization and accelerate Springs Window Fashions' enterprise AI strategy. You will write the code, stand up the pipelines, and get real systems into production—partnering directly with Information Technology, business stakeholders, operations, customer service, product development, and analytics teams to deliver AI capabilities that measurably improve efficiency, elevate customer experiences, and sharpen decision making.

The ideal candidate is a software engineer first who happens to be obsessed with AI, pairing strong engineering fundamentals with hands-on command of machine learning, generative AI, data engineering, automation, and cloud technologies. You move fast and iterate in the open, treating a rough prototype that works as more valuable than a polished plan that doesn't. You thrive in a fast-paced, transformation-oriented environment and consistently turn business problems into production-ready AI solutions rather than pilots that stall in a notebook.

Key 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 tools and techniques, and bring fresh ideas and honest assessments back to the team.
• Prototype ideas quickly—failing fast, learning faster, and turning experiments into working demos.
• Create technical documentation, operational procedures, and knowledge transfer materials.
• Write clean, well-tested code and take part in code reviews to sharpen your craft.
• Pair with senior engineers to learn how enterprise AI systems are designed, shipped, and kept running.
• Grow fast—soak up feedback, ask sharp questions, and share what you learn with the team.

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.
• Manufacturing, supply chain, consumer products, or retail industry experience.
• Experience with MLOps, vector databases, orchestration frameworks, and AI observability platforms.
• Familiarity with data visualization and analytics platforms such as Power BI or Tableau.
• Experience leading enterprise AI transformation initiatives.

Knowledge, Skills & Abilities

• Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.
• Advanced degree in Artificial Intelligence, Machine Learning, or Data Science is a plus—but we care far more about what you have shipped than what you have studied.

How We Work to Deliver a Best Experience: Our Culture

• Our Core Value: We do the right thing, always

• Highly valued leadership skills include:

  • Empowerment: Encourages innovation and continuous learning.  Enables cross-functional collaboration and technical experimentation.
  • Ownership: Owns solutions end to end: if it breaks, you fix it; if it works, you make it better. Delivers secure, scalable, business-aligned AI with real urgency and strong execution discipline.
  • Leadership: Influences technical direction and promotes enterprise AI adoption.  Communicates effectively with both technical and non-technical stakeholders.
  • One Springs Team: Collaborates across departments to drive shared business outcomes.  Builds strong relationships and trust across the enterprise.
  • Continuous Innovation: Stays ahead of emerging AI trends and tools, separating genuine advances from hype. Relentlessly improves AI capabilities, automation, and operational maturity.
  • Speed: Ships iterative value through rapid build-and-deploy cycles, often turning a new technique into a working prototype within days. Balances that speed with the operational stability and governance an enterprise requires.