MillerKnoll is looking to leverage machine learning and generative AI to solve business problems and redefine modern for the 21st century.
Requirements
- Hands-on experience with ML concepts and frameworks such as Scikit-learn, XGBoost, time series forecasting, PyTorch or TensorFlow, and Transformers.
- Familiarity with MLOps and LLMOps best practices.
- Experience building GenAI applications with LangChain, LlamaIndex, or similar frameworks, LLM APIs (OpenAI, Cohere, Anthropic, etc.), embeddings, prompt engineering, fine-tuning, and RAG.
- Solid cloud deployment experience with AWS.
- Experience integrating AI solutions into business systems via APIs and microservices.
- Strong Python skills; proficiency in writing clean, modular, and testable code.
- Knowledge of security and privacy best practices in AI/ML pipelines.
Responsibilities
- Design, build, and optimize machine learning, deep learning, and LLM-based models to solve a variety of business problems.
- Develop GenAI-powered applications using cutting-edge technologies.
- Implement and refine MLOps and LLMOps best practices, including workflow orchestration, experiment tracking, version control, observability, and continuous integration/deployment.
- Develop and maintain scalable data and model pipelines, from initial concept through deployment.
- Rapidly prototype solutions to validate feasibility before full-scale implementation.
- Collaborate with cross-functional teams to deploy models into production via APIs and/or user-facing interfaces.
- Set up and maintain monitoring, alerting, and retraining pipelines to ensure ongoing performance and reliability.
Other
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, or related technical field with 3+ years of hands-on experience in applied machine learning or AI engineering.
- Strong analytical and problem-solving skills with a proactive mindset.
- Excellent communication skills; able to explain technical concepts to diverse audiences.
- High adaptability to evolving tools, frameworks, and industry practices.
- Strong collaboration skills—able to contribute in a highly interdisciplinary and agile setting.
