DevOps Engineer, Machine Learning

Summary

Join Blue Coding, a leading next-gen software developer, and work as a fully remote DevOps Engineer. Collaborate with data scientists and engineers to design and implement scalable ML pipelines and real-time inference services. Automate the end-to-end ML lifecycle, from data ingestion to monitoring. Develop and maintain CI/CD pipelines for seamless integration and deployment of ML models. Troubleshoot and resolve deployment issues and stay updated on the latest advancements in relevant tools and technologies. This role requires strong DevOps and MLOps experience with Terraform and CI/CD, AWS expertise, and programming skills in languages like Python or Java. The ideal candidate is independent, detail-oriented, and a great communicator.

Requirements

• A bachelor’s degree in computer science, information systems, related field, or equivalent work experience
• Strong experience in DevOps and MLOps with Terraform and CI/CD experience
• Strong experience with AWS network & security architecture and containerization technologies
• Experience working with Atlassian Jira, Confluence and Bitbucket pipelines
• Experience in programming skills in languages such as Python, Go, Java, or Scala
• Excellent problem-solving skills and attention to detail
• Excellent communication and interpersonal skills, with the ability to collaborate effectively across different departments
• Experience with big data technologies such as Hadoop and Spark
• Experience with ML frameworks & libraries (e.g., TensorFlow, XGBoost, scikit-learn/sklearn) and MLOps tools
• 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 personal level

Responsibilities

• Collaborate with data scientists, ML engineers, and data engineers to design and implement scalable and reliable ML pipelines and real-time inference services
• 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 of ML models
• Monitor and optimize the performance of deployed models, ensuring they meet business requirements and performance standards
• Assist with setup and configuration of monitoring and optimization systems for deployed models, ensuring they meet business requirements and performance standards
• Troubleshoot and resolve issues related to ML model deployment
• Stay up to date with the latest advancements in relevant tools and technologies

Preferred Qualifications

Certification(s) in AWS Certified Cloud Practitioner and AWS Certified Machine Learning Engineer or AWS Certified DevOps Engineer strongly desired

Benefits

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