MLOps Professional Services Engineer (Cloud & AI Infra)

This job was posted over 30 days ago, so it has most likely closed. See newer jobs like it.

Written by AI from the job title and company, not by Lavendo, so some details may not match this job. The employer's own page has the real description.

Overview

The MLOps Professional Services Engineer at Lavendo will be responsible for bridging the gap between machine learning development and operational deployment in cloud-based environments. This role entails collaborating closely with data scientists, engineers, and business stakeholders to design, implement, and optimize machine learning workflows and infrastructure. The professional will ensure the seamless integration of AI models into operational processes, utilizing best practices in MLOps while enhancing system performance, scalability, and maintainability.

Job Responsibilities

• Design and implement MLOps strategies to automate and streamline model deployment pipelines.
• Collaborate with cross-functional teams to translate machine learning requirements into technical specifications.
• Optimize existing machine learning models for performance and scalability in cloud environments.
• Establish monitoring systems to ensure the reliability and performance of deployed models.
• Provide technical support and guidance to clients during the implementation of AI solutions.
• Document processes, configurations, and best practices to support knowledge sharing and continuous improvement.
• Stay updated with the latest advancements in MLOps and cloud technologies to enhance service offerings.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field; Master’s degree preferred.
• 3+ years of experience in MLOps, machine learning, or cloud infrastructure roles.
• Strong proficiency in Python and familiarity with MLOps frameworks like MLflow, Kubeflow, or TFX.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Knowledge of containerization technologies including Docker and orchestration tools like Kubernetes.
• Understanding of machine learning algorithms and data engineering principles.
• Excellent problem-solving skills and ability to work collaboratively in a team setting.

Technologies & Tools

The MLOps Professional Services Engineer will work with a variety of technologies and tools, including but not limited to: cloud service platforms (AWS, Azure, Google Cloud), MLOps tools (MLflow, Kubeflow), containerization software (Docker), orchestration frameworks (Kubernetes), and programming languages (Python, SQL). The role may also involve the use of configuration management tools and data storage solutions such as PostgreSQL and MongoDB, alongside version control systems like Git.

Ideal Candidates

Ideal candidates for this position will possess a mix of technical expertise and soft skills, including strong analytical capabilities, creativity in addressing complex problems, and excellent communication skills to effectively convey technical concepts to non-technical stakeholders. They should have a proactive attitude towards learning and adapting to new technologies, demonstrating a commitment to continuous improvement. Strong teamwork skills, along with the drive to deliver high-quality solutions, will be essential for success in this fast-paced environment.