Machine Learning Operations Engineer

Summary

Join Blue Coding, a leading next-gen software developer, and work as a Machine Learning Operations Engineer. Collaborate with data scientists and engineers to design and implement scalable and reliable ML pipelines. Automate the end-to-end ML lifecycle, from data ingestion to monitoring. Develop and maintain CI/CD pipelines for seamless integration and deployment. Monitor and optimize deployed models to meet business requirements. Implement best practices for version control and documentation. Troubleshoot and resolve issues related to ML model deployment and performance. Stay updated on the latest advancements in ML Ops tools and technologies. This fully remote and flexible position offers a salary in USD and requires strong knowledge of AWS and experience with big data technologies and ML frameworks.

Requirements

• A bachelor’s degree in computer science, information systems, related field, or equivalent work experience
• Proven experience in ML Ops, DevOps, or related roles
• Experience with big data technologies such as Hadoop and Spark
• Experience with ML frameworks (e.g., TensorFlow, xgboost, sklearn) and ML Ops tools
• Familiarity with AWS and containerization technologies
• Strong programming skills in Python, Java, Scala, or Go
• Excellent problem-solving skills and attention to detail
• Excellent communication and interpersonal skills, with the ability to collaborate effectively across different departments
• Experience working with Atlassian Jira, Confluence, and Bitbucket
• Self-motivated and able to work independently and in a team environment
• Flexibility to adapt to changing priorities and handle multiple tasks simultaneously
• Strong sense of personal responsibility and accountability, delivering excellent work both at a team level and individual level

Responsibilities

• Collaborate with data scientists, ML engineers, and data engineers to design and implement scalable and reliable ML pipelines
• Automate the end-to-end ML lifecycle, including data ingestion, model training, evaluation, deployment, and monitoring
• Develop and maintain CI/CD pipelines for ML models to ensure seamless integration and deployment
• Monitor and optimize the performance of deployed models, ensuring they meet business requirements and performance standards
• Implement best practices for version control, reproducibility, and documentation of ML experiments
• Troubleshoot and resolve issues related to ML model deployment and performance
• Stay current with the latest advancements in ML Ops tools and technologies

Benefits

• Salary in USD
• Flexible schedule (within US Time zones)
• 100% Remote