Senior AI Engineer

The Best Experience Company Our tagline is “The Best Experience Company.” More than just a set of words, it represents the essence of who we are at Springs Window Fashions. As North America’s premier window covering company, we’re committed to creating the Best Experience for our associates, consumers and end users, business partners, and communities. We want you to join our team of passionate self-starters who believe the world is full of Best Experience opportunities. So, if you’re excited about the thought of a Best Experience career with a team focused on creating Best Experiences for all, we want to hear from you!

Description

The Best Experience Company Our tagline is “The Best Experience Company.” More than just a set of words, it represents the essence of who we are at Springs Window Fashions. As North America’s premier window covering company, we’re committed to creating the Best Experience for our associates, consumers and end users, business partners, and communities. We want you to join our team of passionate self-starters who believe the world is full of Best Experience opportunities. So, if you’re excited about the thought of a Best Experience career with a team focused on creating Best Experiences for all, we want to hear from you!

Position Summary

This is an engineering role, not a research role. The Sr AI Engineer is a hands‑on technical leader who ships enterprise‑scale AI into production and owns the architecture, governance, and engineering practices that turn ambitious ideas into working systems. You will drive AI adoption across the organization, mentor other engineers by building alongside them, and partner 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

• Design, build, ship, and own enterprise AI and machine learning solutions in production.
• Build and operationalize generative AI applications using large language models (LLMs), retrieval‑augmented generation (RAG), copilots, and intelligent automation that real people across the business actually use.
• Partner with business leaders to find and prioritize the highest‑value AI use cases, and have the judgment to say no to the ones that aren’t.
• Stand up scalable AI pipelines, APIs, and integrations with enterprise platforms and business applications quickly, then improve them in the open.
• 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.
• Implement AI governance, model monitoring, security, and responsible AI practices.
• Optimize model performance, scalability, reliability, and operational efficiency.
• Evaluate emerging AI tools almost as fast as they ship, and give a clear, honest read on what is real and what is hype before recommending enterprise adoption.
• Drive rapid experimentation and prototyping across the organization, failing fast, learning faster, and moving to the next iteration.
• Create technical documentation, operational procedures, and knowledge transfer materials.
• Mentor other engineers by building alongside them, not by lecturing, and continually raise the bar on AI engineering practices.

Requirements

Required

• 8–10+ years overall technology experience
• 5+ years specifically building—not just studying—AI/ML systems
• Proven track record deploying AI at enterprise scale in production, not pilots that stalled in a notebook
• Experience leading technical initiatives or teams that people want to follow, not just report to
• Strong experience with AI architecture and distributed systems
• Hands‑on experience operationalizing generative AI at scale: LLMs, RAG, copilots, and automation
• Able to explain what you built to an executive in two sentences and to an engineer in two hundred

Preferred

• 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.
• AI governance frameworks
• FinOps for AI workloads
• Multi‑cloud AI strategy
• Experience building internal AI platforms or copilots

How We Work to Deliver a Best Experience: Our Culture

• 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.