Machine Learning Operations Engineer ( ML Ops)

MLOps EngineerLocal to MI or willing to relocate12+ months contractHybrid - 1-2 days/week in the office and 3-4 days work from home.Immediate hireAzure experience is a must, specifically Azure Container Apps.Strong DevOps practices and tools experience: Red Hat Linux, Jenkins (building Jenkins instances), Ansible playbooks for automation.Problem solving skills required.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.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, especially Azure Container Apps.Good experience with containerization technologies such as Docker and orchestration tools like Kubernetes.Proficiency in automation tools like Ansible playbooks, Jenkins (building and configuring Jenkins instances from scratch), Docker Compose, Artifactory, etc.Strong knowledge of DevOps practices and tools for continuous integration, continuous deployment (CI/CD), and infrastructure as code (IaC).Red Hat Linux (RPM based) experience highly preferred.Experience working in an air-gapped environment is highly preferred.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) is a plus.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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