MLOps Professional Services Engineer (Cloud & AI Infra)

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Position Overview

Lavendo is seeking a skilled MLOps Professional Services Engineer (Cloud & AI Infra) to join our dynamic team. In this role, you will be responsible for delivering impactful MLOps solutions and AI infrastructure enhancements to our clients. You will work closely with cross-functional teams to design, implement, and optimize machine learning workflows, ensuring scalability and efficiency across all MLOps initiatives. Your expertise will empower our clients to harness the full potential of their AI technologies, driving innovation and improving business outcomes.

Job Responsibilities

• Design, implement, and maintain MLOps pipelines for the continuous integration and deployment of machine learning models.
• Collaborate with data scientists and engineers to streamline data processing and model training workflows.
• Monitor and resolve issues related to model performance and operational workflows.
• Provide technical guidance and support to clients on best practices for AI infrastructure deployment.
• Contribute to the development of tools and frameworks that enhance MLOps capabilities.
• Evaluate and recommend cloud-based solutions for machine learning and AI workloads.
• Conduct training sessions and workshops for clients and internal teams to promote MLOps methodologies.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, or a related field; Master’s degree preferred.
• Minimum of 3 years of experience in MLOps, DevOps, or related roles in cloud environments.
• Proficiency in programming languages such as Python, Java, or Scala for AI/ML development.
• Deep understanding of cloud computing platforms like AWS, Azure, or Google Cloud.
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Experience with CI/CD tools and practices for machine learning workflows.
• Strong analytical and problem-solving skills, with an ability to communicate technical concepts to non-technical stakeholders.

Technologies & Tools

The MLOps Professional Services Engineer will utilize a variety of technologies and tools, including but not limited to: TensorFlow, PyTorch, Apache Airflow, MLflow, Jenkins, Docker, Kubernetes, AWS SageMaker, Azure ML, and Google AI Platform. Familiarity with version control systems like Git as well as data processing frameworks such as Apache Spark will also be advantageous.

Ideal Candidates

The ideal candidate for this position possesses a proactive mindset and excels in a collaborative environment. Strong communication and interpersonal skills are essential for effectively advising clients and working within diverse teams. A deep commitment to continual learning in the rapidly evolving landscape of AI and cloud technologies is highly valued. Additionally, candidates should exhibit versatility in problem-solving and a strong passion for optimizing machine learning operations to deliver client success.