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 bridging the gap between machine learning model development and operational deployment. This position entails collaborating closely with internal teams and clients to design, implement, and optimize end-to-end MLOps solutions. The successful candidate will have a strong understanding of cloud infrastructures and artificial intelligence applications, ensuring scalable, reliable, and efficient machine learning models in production environments.

Job Responsibilities

• Collaborate with cross-functional teams to gather requirements and design MLOps frameworks tailored to client needs.
• Implement machine learning lifecycle management practices, including model versioning, deployment, and monitoring.
• Utilize cloud platforms to design and optimize infrastructure for machine learning workloads.
• Develop automation tools and CI/CD pipelines for seamless integration and continuous deployment of ML models.
• Provide technical guidance and support to customers on best practices for deploying AI solutions.
• Monitor and troubleshoot MLOps systems in production, ensuring optimal performance and uptime.
• Stay abreast of industry trends and emerging technologies to recommend innovative solutions to clients.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• Minimum of 3 years of professional experience in MLOps, Machine Learning engineering, or related fields.
• Proficiency in cloud services such as AWS, GCP, or Azure focused on MLOps.
• Strong programming skills in languages such as Python, R, or SQL.
• Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Solid understanding of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Excellent problem-solving skills and the ability to work collaboratively with diverse teams.

Technologies & Tools

In this role, candidates will work with a range of technologies and tools which are critical for effective MLOps practices. These include cloud platforms like AWS, Google Cloud Platform, or Microsoft Azure; containerization tools such as Docker and Kubernetes; machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn; and CI/CD tools for automation and deployment. Familiarity with monitoring and logging tools will also be important to ensure the performance of ML applications in production.

Ideal Candidates

The ideal candidates for the MLOps Professional Services Engineer position will possess a blend of technical expertise and interpersonal skills. They should be adaptable, proactive, and have a strong analytical mindset to troubleshoot complex systems. Successful individuals will demonstrate effective communication skills to articulate technical concepts clearly to both technical and non-technical stakeholders. A passion for innovation and a commitment to continuous improvement will also be key attributes in a candidate who aims to excel in this dynamic environment.