We are seeking a highly skilled MLOps Engineer to lead the design, development, and deployment of machine learning frameworks, tools, and pipelines for end-to-end ML lifecycle management. In this role, you will apply MLOps technologies to operationalize AI solutions at scale, ensuring seamless integration with client business processes and applications. Your technical expertise will contribute to the creation and delivery of MLOps solutions that drive impactful outcomes for our customers.
Key Responsibilities
Infrastructure and Environment Management
- Design and implement scalable and reliable infrastructure for machine learning models and applications.
- Ensure seamless integration of machine learning models into production systems.
Continuous Integration/Continuous Deployment (CI/CD)
- Develop and maintain CI/CD pipelines to automate the deployment of machine learning models.
- Implement version control and release management practices for machine learning assets.
Model Deployment and Monitoring
- Deploy machine learning models to production environments and monitor their performance.
- Implement solutions to detect and address issues related to model drift, data quality, and system health.
Collaboration with Data Scientists and Engineers
- Work closely with data scientists to understand model requirements and facilitate the transition from research to production.
- Collaborate with software engineers to integrate machine learning solutions into existing applications.
Security and Compliance
- Apply security best practices for machine learning systems.
- Ensure compliance with relevant regulations and industry standards.
Automation and Scripting
- Develop automation scripts and tools to streamline MLOps processes.
- Implement best practices for code and configuration management.
Troubleshooting and Incident Response
- Provide support for troubleshooting issues related to machine learning models in production.
- Develop and implement incident response plans for machine learning systems.
Documentation
- Maintain comprehensive documentation for MLOps processes, workflows, and configurations.
Qualifications
- Bachelor’s/Master’s degree in Computer Science, Engineering, or a related field.
- 6-8 years of experience managing machine learning projects end-to-end, with the last 18 months focused on MLOps.
- Proven experience with MLOps platforms and tools (e.g., MLflow, Kubeflow, TensorFlow, PyTorch).
- Proficiency in scripting languages (e.g., Python, Bash).
- Experience with CI/CD tools (e.g., Jenkins, GitLab CI).
- Understanding of cloud platforms (e.g., AWS, Azure, GCP).
- Strong problem-solving and troubleshooting skills.
- Excellent communication and collaboration abilities.
- Knowledge of security practices in machine learning systems.
Preferred Skills
- Previous experience in a similar role within the ML/AI domain.
- Familiarity with AI infrastructure and deployment.