MLOps Engineer - Michigan, US

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).