Remote MLOps Engineer

Summary

Join us at Encora as a Machine Learning Engineer to facilitate the seamless transition of machine learning models from development to production. As a key member of our team, you will automate and streamline model deployment, build robust data pipelines, and ensure regulatory compliance.

Requirements

• Bachelor’s Degree in Computer Science, Data Science, Engineering, or a related field
• Coursework should include advanced mathematics, statistics, computer programming, machine learning, and possibly courses specifically on MLOps or data engineering
• Advanced Degree (Optional but advantageous) in Machine Learning, Data Science, or Artificial Intelligence
• Cloud Certifications such as AWS Certified Machine Learning - Specialty, Google Professional Machine Learning Engineer, or Azure AI Engineer Associate
• MLOps Certifications focusing on specific tools and platforms like TensorFlow Developer Certificate, Kubeflow, or certifications on specific aspects of data engineering and machine learning operations
• Programming Skills in Python with knowledge of libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn
• DevOps Tools experience with Docker, Kubernetes, and CI/CD tools (e.g., Jenkins, GitLab CI)
• Data Management knowledge of handling big data technologies and databases (e.g., Hadoop, Spark, MySQL, MongoDB)
• Monitoring Tools familiarity with monitoring tools like Prometheus, Grafana, or ELK stack

Responsibilities

• Automate and streamline the process of deploying machine learning models into production environments, ensuring they run reliably at scale
• Build and maintain robust data pipelines for continuous training and deployment of machine learning models, including Generative AI models such as LLMs (Large Language Models)
• Implement monitoring solutions to track the performance and health of models in production, quickly identifying and addressing degradation or failures
• Manage version control of both data and models. Use tools like MLflow or DVC to track experiments, manage the lifecycle of machine learning models, and ensure reproducibility
• Work closely with data scientists, AI researchers, and software engineers to ensure that ML systems are well-integrated with the company’s software infrastructure
• Optimize machine learning infrastructure for performance and cost, utilizing cloud technologies and services efficiently
• Ensure that the machine learning deployments comply with relevant data privacy and protection regulations, particularly when handling sensitive or personal data
• Establish and promote MLOps best practices within the team, including guidelines for code quality, deployment procedures, and security measures