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Azure MLOps Specialist

2 months ago

Ann Arbor, Michigan, United States JRD Systems Full time

Job Title - Machine Learning Operations Engineer

Base Location – Ann Arbor, Michigan (Hybrid)

Duration – 6+ months Contract to Hire

**Candidates must be local to Michigan

Hybrid - **3 days in Office, 2 days from Home. This role is designed for long-term engagement.

Interview Process - **Two rounds: Initial Virtual Screening followed by In-Person Interview (Travel may be required for non-local candidates).

Job Overview:

JRD Systems is in search of a proficient Machine Learning Operations Engineer with a strong focus on Azure ML. This position plays a pivotal role in enhancing our analytical capabilities, concentrating on the establishment of MLOps Key Performance Indicators (KPIs), implementing robust model monitoring and alerting systems, and investigating opportunities for online learning. The ideal candidate will possess extensive expertise in Azure ML, Azure DevOps, Azure Container Apps, and will be well-versed in automation tools such as Jenkins, alongside a solid foundation in containerized software deployments and CI/CD pipelines.

As a Machine Learning Operations Engineer, your primary responsibility will be to bridge the gap between data science and IT, ensuring effective and efficient deployment of machine learning models. You will work collaboratively with cross-functional teams to design, implement, and maintain scalable machine learning pipelines and infrastructure.

Key Responsibilities:

  • Design, implement, and sustain comprehensive machine learning pipelines for model training, validation, and deployment.
  • Collaborate with data scientists, software engineers, and DevOps engineers to seamlessly integrate machine learning models into production environments.
  • Develop automation tools and frameworks to enhance the machine learning workflow, including data preprocessing, feature engineering, model training, and evaluation.
  • Optimize model performance and scalability by utilizing cloud computing resources and distributed computing methodologies.
  • Establish monitoring and logging solutions to assess model performance, data quality, and system health in production settings.
  • Oversee model versioning, experimentation, and reproducibility through version control systems and experiment tracking tools.
  • Stay informed about the latest advancements and technologies in machine learning, cloud computing, and software engineering, and integrate them into the MLOps processes.
  • Provide technical mentorship and guidance to junior team members on MLOps best practices.

Qualifications:

  • Bachelor's degree or higher in computer science, engineering, mathematics, or a related discipline.
  • Strong programming capabilities in languages such as Python, Java, or Scala.
  • Demonstrated experience as an MLOps Engineer, particularly with Azure ML and associated Azure technologies.
  • Familiarity with containerization technologies like Docker and orchestration tools such as Kubernetes.
  • Proficiency in automation tools including JIRA, Ansible, Jenkins, Docker Compose, Artifactory, etc.
  • Understanding of DevOps practices and tools for continuous integration, continuous deployment (CI/CD), and infrastructure as code (IaC).
  • Experience with version control systems like Git and collaboration platforms such as GitLab or GitHub.
  • Exceptional problem-solving abilities and capacity to thrive in a fast-paced, collaborative environment.
  • Strong communication skills with the ability to convey 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.