MLOps Engineer with API- Initial Remote

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Job Overview

Stellent IT is seeking a skilled and motivated MLOps Engineer to join our team on a remote basis. In this role, you will be pivotal in integrating machine learning models into our deployment pipelines, ensuring that operations are both efficient and scalable. Your expertise will help bridge the gap between data engineering and machine learning teams, fostering collaboration to streamline processes and enhance model performance in production environments. You will take charge of maintaining and optimizing our MLOps infrastructure, developing tools, and providing insights to improve data workflows.

Job Responsibilities

• Design and implement MLOps frameworks to optimize the deployment of machine learning models.
• Collaborate with data scientists and IT teams to ensure the seamless integration of ML models into production.
• Monitor and analyze model performance, troubleshooting issues as they arise.
• Automate data workflows and model training processes to improve efficiency.
• Implement CI/CD practices for ML models and maintain version control.
• Create and maintain documentation for MLOps processes and models.
• Stay updated with industry trends in MLOps and machine learning deployment.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
• Proven experience as an MLOps Engineer or in a similar role.
• Strong understanding of machine learning algorithms and frameworks.
• Proficiency in programming languages such as Python or R.
• Experience with cloud platforms (AWS, Azure, GCP) and their ML services.
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Knowledge of CI/CD tools and version control systems like Git.
• Strong analytical and problem-solving skills.

Technologies & Tools

The MLOps Engineer will utilize a variety of technologies and tools to perform their duties effectively. Some of the tools include machine learning libraries such as TensorFlow and PyTorch, data orchestration tools like Apache Airflow, cloud services (AWS, Azure, GCP), containerization technologies (Docker, Kubernetes), and CI/CD tools (Jenkins, GitLab CI/CD) for automating deployment processes. Familiarity with ML monitoring tools and data visualization platforms will also be beneficial for overseeing model performance and data analytics activities.

Ideal Candidates

The ideal candidate for the MLOps Engineer position at Stellent IT will be a proactive thinker with strong technical skills and a collaborative attitude. They will possess excellent communication skills to liaise effectively with cross-functional teams and an aptitude for continuous learning to keep up with rapid advancements in the field of MLOps. Additionally, a good fit will demonstrate problem-solving abilities, attention to detail, and a passion for leveraging machine learning technologies to drive impactful results across the organization.