MLOps Engineer
- Local to MI or willing to relocate
- 12+ months contract
- Hybrid - 1-2 days / week in the office and 3-4 days work from home.
- Immediate hire
- Azure experience is a must. Azure Container Apps
- Strong DevOps Practices and Tools exp. Red Hat Linux, Jenkins ( Building Jenkins Instances) , Ansible Playbooks for automation
- Problem Solving
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 specially Azure Container Apps experience.
- 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 Air-Gapped environment 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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