Machine Learning Operations Engineer

Here are some of the exciting day-to-day challenges you will face in this role:

• 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.

You will shine if you have:

• 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 

What we offer:

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