D&AI
Michigan
Permanent
MLOps Engineer - Michigan
Our client is a leading industrial manufacturing company based in Michigan, committed to driving innovation and efficiency within the manufacturing sector. Known for producing high-quality industrial products and solutions, they are at the forefront of integrating cutting-edge technologies to optimize their operations and product offerings. With a strong focus on digital transformation, they are now looking to expand their team with a talented MLOps Engineer to support their machine learning (ML) initiatives.
Role Responsibilities:
- Design, build, and maintain scalable and efficient ML infrastructure for deploying and monitoring models in production.
- Work closely with data science teams to automate model training, evaluation, and deployment pipelines.
- Develop and implement CI/CD practices for ML models, ensuring they are versioned, reproducible, and compliant with organizational standards.
- Monitor, troubleshoot, and resolve issues related to ML models in production, ensuring minimal downtime.
- Integrate and operationalize Generative AI (GenAI) models, ensuring they meet performance and security standards for production use.
- Manage data pipelines to ensure consistent and reliable data flow for model training and prediction.
Key Skills:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
- Proven experience in MLOps, DevOps, or related roles with a focus on machine learning infrastructure.
- Proficiency in cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization tools like Docker and Kubernetes.
- Proficiency in programming languages like Python, with a focus on ML deployment and automation & scripting language i.e Bash
- Knowledge of ML life cycle management frameworks (e.g. MLFlow) and ML platforms (e.g. Azure Machine Learning, Databricks Model Hosting).
- Familiarity with generative AI, Large Language Models (LLMs) and other Foundation Model applications.
- Experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
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