We are seeking a skilled Machine Learning Operations (ML Ops) Engineer with 3+ years of experience to join our dynamic team. The ideal candidate will be responsible for streamlining the development, deployment, and management of machine-learning models in production environments. You will work closely with data scientists, software engineers, and IT operations to ensure our ML models are robust, scalable, and maintainable.
Responsibilities
- Model Deployment: Implement and manage the deployment of machine learning models into production environments.
- Automation: Develop and maintain automated pipelines for attribute and model deployment.
- Monitoring: Monitor the performance and health of attributes and deployed models, ensuring they meet performance and accuracy standards.
- Collaboration: Work with data scientists to understand model requirements and translate them into scalable solutions.
- Optimization: Optimize model performance and resource utilization in production.
- Documentation: Maintain comprehensive documentation of ML operations processes and workflows.
- Compliance: Ensure all ML operations comply with industry standards and regulatory requirements.
Qualifications
- Experience: Minimum of 3 years of experience in ML operations, data engineering, or a similar role. Demonstrable Experience with CI/CD pipelines and DevOps practices.
- Education: Bachelor’s degree in Computer Science, Engineering, or a related field. A Master’s degree is a plus.
- Technical Skills: Proficiency in Python, R and other ML frameworks. Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
- Tools: Familiarity with MLFlow, Databricks, SageMaker and other ML tools.
- Soft Skills: Strong problem-solving skills, excellent communication, and the ability to work collaboratively in a team environment.
Other Desirable Qualifications:
- Experience working in a startup environment a plus.
- Must meet requirements of company background check policy requirements.
- Experience in the financial services industry.
- Knowledge of regulatory requirements related to consumer lending.
Success Measures
- Ability and willingness to self-start and self-manage in a high volume, data-driven environment.
- Ability to enhance portfolio outcomes through analytics and operational acumen.
- Ability to scope, lead, manage, and execute assigned projects with effective data visualization.
- Achieve goals while promoting a positive attitude true to our mission and core values.
