-Job Description: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 Client 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.