MLOps Professional Services Engineer (Cloud & AI Infra)

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Job Overview

The MLOps Professional Services Engineer (Cloud & AI Infra) at Lavendo will play a crucial role in bridging the gap between data science and operations by designing, implementing, and maintaining machine learning pipelines and infrastructures. This position requires a deep understanding of both cloud environments and AI technologies, ensuring robust and scalable solutions that support the deployment of machine learning models. The ideal candidate will collaborate closely with cross-functional teams to optimize processes and contribute to the overall strategic goals of the organization.

Job Responsibilities

• Design and implement end-to-end machine learning workflows and infrastructure on cloud platforms.
• Collaborate with data scientists to develop and operationalize machine learning models.
• Automate processes for data collection, model training, and deployment.
• Monitor and maintain the performance of deployed models, ensuring they meet business needs.
• Facilitate knowledge sharing across teams to establish best practices and standards in MLOps.
• Provide technical support and guidance to clients during the implementation of MLOps solutions.
• Stay updated with industry trends, tools, and technologies to continuously improve practices.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 3+ years of experience in machine learning operations or related roles.
• Proficiency in cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services.
• Strong programming skills in Python and experience with relevant ML libraries (e.g., TensorFlow, PyTorch).
• Solid understanding of CI/CD practices and experience with tools like Jenkins, Git, or similar.
• Familiarity with container technologies such as Docker and orchestration tools like Kubernetes.
• Excellent problem-solving skills and ability to work in a dynamic team environment.

Technologies & Tools

The MLOps Professional Services Engineer will primarily work with a range of technologies and tools, including but not limited to cloud platforms such as AWS, Azure, or Google Cloud, machine learning frameworks like TensorFlow and PyTorch, data orchestration tools like Apache Airflow, and containerization technologies such as Docker and Kubernetes. Familiarity with CI/CD tools and version control systems, including Git, will also be essential to ensure efficient workflows.

Ideal Candidates

The ideal candidates for this position possess a strong blend of technical and interpersonal skills. They are proactive problem solvers with a passion for optimizing machine learning processes and a keen interest in leveraging cloud technologies. Successful professionals in this role should be adaptable to change, thrive in a collaborative environment, and demonstrate excellent communication skills to effectively liaise with both technical and non-technical stakeholders. Moreover, a commitment to staying informed about the latest advancements in MLOps will be highly valued.