Machine Learning Engineer

2 weeks ago

Ann Arbor, United States JRD Systems Full time

This role is central to strengthening our analytics capabilities, focusing on establishing MLOps Key Performance Indicators (KPIs), , implementing model monitoring and alerting systems, and exploring avenues towards online learning. The successful candidate will be an expert in Azure ML, Azure DevOps, Azure Container Apps, and familiar with automation tools like Jenkins, with a strong background in containerized software deployments and CI/CD pipelines.

As an MLOps Engineer, you will be responsible for bridging the gap between data science and IT, ensuring that machine learning models are deployed effectively and efficiently. You will collaborate with cross-functional teams to design, implement, and maintain scalable machine learning pipelines and infrastructure.

**Responsibilities: **

Design, implement, and maintain end-to-end machine learning pipelines for model training, validation, and deployment.

Collaborate with data scientists, software engineers, and DevOps engineers to integrate machine learning models into production systems.

Develop automation tools and frameworks to streamline the machine learning workflow, including data preprocessing, feature engineering, model training, and evaluation.

Optimize model performance and scalability by leveraging cloud computing resources and distributed computing techniques.

Implement monitoring and logging solutions to track model performance, data quality, and system health in production.

Manage model versioning, experimentation, and reproducibility using version control systems and experiment tracking tools.

Stay up-to-date with the latest trends and technologies in machine learning, cloud computing, and software engineering, and incorporate them into the MLOps workflow.

Provide technical guidance and mentorship to junior team members on best practices for MLOps.

**Qualifications: **

Bachelor's degree or higher in computer science, engineering, mathematics, or related field.

Strong programming skills in languages such as Python, Java, or Scala.

Proven experience as an MLOps Engineer, specifically with Azure ML and related Azure technologies.

Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes.

Proficiency in automation tools like JIRA, Ansible, Jenkins, Docker compose, Artifactory, etc.

Knowledge of DevOps practices and tools for continuous integration, continuous deployment (CI/CD), and infrastructure as code (IaC).

Experience with version control systems such as Git and collaboration tools like GitLab or GitHub.

Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.

Strong communication skills and ability to effectively communicate technical concepts to non-technical stakeholders.

Certification in cloud computing (e.g., AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer).

Knowledge of software engineering best practices such as test-driven development (TDD) and code reviews.

Experience with Rstudio/POSIT connect, RapidMiner.

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