Director, Lean-AI Engineering & Architecture

Why join us?

Our purpose is to design for the good of humankind. It's the ideal we strive toward each day in everything we do. Being a part of MillerKnoll means being a part of something larger than your work team, or even your brand. We are redefining modern for the 21st century. And our success allows MillerKnoll to support causes that align with our values, so we can build a more sustainable, equitable, and beautiful future for everyone.

Job Title: Director, Lean-AI Engineering & Architecture

Location: US Core Site (preference MI, Chicago)
Team: Lean-AI Program
Reports to: VP, Enterprise Technology & Data Enablement

About the Team

The Lean-AI Program is MillerKnoll's center of excellence for practical, responsible AI adoption. The team includes data scientists, ML/AI engineers, business process automation, and MillerKnoll Professional Systems (MKPS). Together, we rapidly experiment, build, and deploy AI solutions that deliver measurable value. Our focus is lean, business-driven innovation-reducing human struggle, avoiding unnecessary complexity, and embedding AI into enterprise systems, processes, and experiences.

About the Role

MillerKnoll is seeking a seasoned technology leader to shape the future of AI adoption at enterprise scale. The Director, Lean-AI Engineering & Architecture will lead a high-impact team focused on designing, integrating, and scaling ML/AI and GenAI solutions across MillerKnoll's ecosystem.

The ideal candidate has 10-12 years of experience in software engineering or architecture, with a strong track record in modern cloud frameworks, experience with complex distributed data ecosystems, and at least 4 years of applied ML/AI delivery in business settings. This leader will shape how MillerKnoll develops and integrates AI into its enterprise systems and processes- building reusable accelerators, frameworks, and governance models while ensuring solutions are secure, ethical, and business-aligned.

Key Responsibilities

Technical Strategy & Leadership

• Define and execute the Lean-AI Program vision, balancing rapid innovation with enterprise stability.
• Establish enterprise-ready patterns for GenAI and applied AI adoption (e.g., reusable prompts, orchestration frameworks, microservices).
• Lead, mentor, and grow a team of ML/AI and GenAI engineers and data scientists.
• Collaborate with enterprise architects, data engineers, digital product teams, and business process leaders to align Lean-AI initiatives with enterprise priorities.
• Promote responsible AI practices, ensuring security, ethics, and compliance.

Solution Delivery

• Lead the design, development, and deployment of AI/ML solutions.
• Oversee the creation of reusable frameworks, accelerators, and patterns for efficient Lean-AI delivery.
• Ensure solutions move seamlessly from prototype to production with enterprise-grade MLOps/LLMOps practices.
• Drive integration of AI capabilities into adjacent enterprise systems and workflows.
  

Architecture & Integration

• Establish ML/AI architectural standards and best practices for deploying AI within a modern cloud-native environment.
• Leverage cloud-native services (AWS preferred) to deliver distributed, event-driven architectures.
• Identify and implement integration points across enterprise data platforms (Snowflake, Salesforce, Infor-LN, Jira, etc.).
• Stay ahead of emerging AI/GenAI tools, frameworks, and ecosystem innovations, continuously evolving the Lean-AI roadmap.

Required Technical Skills

• Bachelor's or master's degree in computer science, Engineering, or a related technical field.
• 10-12 years in software engineering or architecture, with a strong focus on modern cloud-native frameworks (AWS preferred).
• 4+ years of practical ML/AI experience, including developing and deploying models into production environments.
• Proven track record of integrating ML/AI solutions into enterprise business processes and systems.
• Expertise in enterprise-scale data pipelines, distributed data systems, and modern data platforms (Snowflake, Kafka, microservices, event-driven patterns).
• Hands-on knowledge of ML/AI frameworks (e.g., PyTorch, TensorFlow, RAG, vector databases, Transformers, LangChain, LLM APIs).
• Strong understanding of MLOps/LLMOps practices, CI/CD for AI, monitoring, and model lifecycle management.

Soft Skills & Qualities

• Strong leadership and people development skills.
• Strategic thinker with the ability to execute.
• Excellent communication and storytelling skills across multiple levels of technical and business audiences.
• Passion for pragmatic innovation, rapid iteration, and continuous learning.
• Commitment to responsible AI practices, security, and knowledge sharing.
• Strong analytical and problem-solving skills with a proactive mindset.
• High adaptability to evolving tools, frameworks, and industry practices.
• Strong collaboration skills-able to contribute in a highly interdisciplinary setting.

Who We Hire?

Simply put, we hire qualified applicants representing a wide range of backgrounds and abilities. MillerKnoll is comprised of people of all abilities, gender identities and expressions, ages, ethnicities, sexual orientations, veterans from every branch of military service, and more. Here, you can bring your whole self to work. We're committed to equal opportunity employment, including veterans and people with disabilities.

MillerKnoll complies with applicable disability laws and makes reasonable accommodations for applicants and employees with disabilities. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact MillerKnoll Talent Acquisition at careers_help@millerknoll.com.