Senior Machine Learning Operations Engineer
5 days ago
The Senior Machine Learning Operations Engineer is responsible for designing, developing, deploying, and monitoring machine learning models. The engineer works closely with data analysts, ML engineers in analytics exploration, and collaborates with software and data engineers to ensure infrastructure and data pipelines are structured to deploy machine learning solutions.
Key Responsibilities:- Business Requirements Analysis: Translate business and functional needs into machine learning problem statements.
- Problem Statement Translation: Translate complex machine learning problem statements into specific deliverables and requirements.
- Algorithm Assessment and Improvement: Assess and improve algorithms, feature sets, and validation metrics received from data analysts and ML engineers in analytics exploration to ensure their efficacy for specific problems; modify and retrain models as needed for deployment.
- Code Conversion and Infrastructure Setup: Convert exploration team's code into robust, production-level solutions, including CI/CD pipeline integration and infrastructure setup for training and monitoring to meet enterprise requirements.
- MLOps Infrastructure Maintenance: Oversee the ongoing enhancement and maintenance of our model-agnostic MLOps infrastructure and Python package, ensuring high standards of performance and reliability.
- Collaboration and Deployment: Collaborate with development teams to test and deploy machine learning models.
- Performance Evaluation: Create metrics to continuously evaluate the performance of machine learning solutions.
- Compliance and Governance: Ensure adherence to performance standards and compliance to data security and model governance requirements.
- Staying Up-to-Date: Keep abreast with new tools, algorithms, and techniques in machine learning and work to implement them in the organization.
More than five years of experience in developing and deploying enterprise-scale machine learning solutions. A bachelor's degree in software engineering, data science, or computer science, or a related quantitative field, is required. An advanced degree in a related quantitative field is preferred. A strong understanding of probability and statistical models. Proficiency in machine learning algorithms. Ability to run experiments scientifically and analyze results. Experience in code reproducibility and version control. Advanced programming skills with Python and SQL. Proficiency in Linux, Bash, and Docker. Proficiency with AWS ML technology stack or equivalent. Proficiency with CI/CD and Infrastructure as Code. Strong ability to collaborate effectively across multiple teams and stakeholders, including analytics teams, development teams, and operations. Exceptional communication skills and ability to translate technical concepts into appropriate language for all stakeholders. Required to be highly creative and collaborative. Demonstrate good judgment, a sense of urgency. Commitment to high standards of ethics, regulatory compliance, customer service, and business integrity.
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