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 pivotal role in driving the implementation, optimization, and maintenance of machine learning operations within our cloud and AI infrastructure. This role requires a deep understanding of both machine learning concepts and cloud technologies, enabling the engineer to collaborate effectively with clients and internal teams. The individual will be responsible for ensuring that the deployment of AI solutions is seamless, scalable, and sustainable while providing expert guidance on best practices, architecture, and integration.

Job Responsibilities

• Design and implement robust machine learning workflows using MLOps principles.
• Collaborate with data scientists and software engineers to streamline model deployment.
• Optimize and monitor the performance of machine learning models in production environments.
• Manage cloud resources efficiently to support AI infrastructure and ensure scalability.
• Provide technical consulting and guidance to clients on MLOps best practices.
• Develop automated deployment pipelines for model training and inference.
• Troubleshoot and resolve issues related to model performance and infrastructure.
• Document processes, architectures, and workflows to promote knowledge-sharing.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field; Master’s degree preferred.
• 3+ years of experience in MLOps or a similar role related to machine learning operations.
• Proficiency in cloud platforms such as AWS, Azure, or Google Cloud.
• Strong understanding of machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.
• Experience with containerization technologies such as Docker and orchestration platforms like Kubernetes.
• Familiarity with CI/CD practices and tools to automate deployment workflows.
• Solid programming skills in languages such as Python, Java, or Scala.
• Excellent communication and interpersonal skills to collaborate with cross-functional teams.

Technologies & Tools

In this role, the MLOps Professional Services Engineer will utilize a variety of technologies and tools which may include but are not limited to AWS SageMaker, Kubeflow, Docker, Kubernetes, Git, Jenkins, Terraform, and various machine learning libraries. The ability to leverage these tools will be essential for the successful design, deployment, and management of machine learning models across the cloud infrastructure.

Ideal Candidates

The ideal candidate for this position will possess a proactive and analytical mindset, demonstrating a passion for innovation in the field of AI and machine learning. They should have strong problem-solving skills, the ability to work under pressure, and a collaborative spirit conducive to teamwork. A successful candidate will be adaptable to evolving technologies and willing to stay current with industry trends, continuously seeking knowledge to enhance their skills and deliver value to both the company and its clients.