ML Ops Engineer

As an MLOps Engineer you will be responsible for bridging the gap between data science and operations. You will collaborate closely with data scientists machine learning engineers and software developers to deploy manage and monitor machine learning models in production environments. Your role will involve implementing automation pipelines ensuring scalability reliability and performance of machine learning systems and optimizing workflows for efficiency. Additionally you will play a key role in maintaining data integrity security and compliance throughout the machine learning lifecycle.

Responsibilities:

1. Collaborate with crossfunctional teams to understand machine learning model requirements and deploy scalable solutions in production environments.
   
2. Develop and maintain automation pipelines for model training testing deployment and monitoring.
   
3. Implement continuous integration and continuous deployment (CI/CD) processes to streamline model deployment and updates.
   
4. Optimize machine learning infrastructure for performance scalability and costeffectiveness.
   
5. Design and implement monitoring and alerting systems to track model performance data quality and system health.
   
6. Ensure data integrity security and compliance with regulatory requirements (e.g. GDPR HIPAA) throughout the machine learning lifecycle.
   
7. Troubleshoot issues related to model deployment infrastructure and data pipelines.
   
8. Stay updated on emerging technologies and best practices in MLOps and contribute to the evolution of the MLOps framework within the organization.
   
9. Collaborate with DevOps teams to integrate machine learning workflows into existing CI/CD pipelines.
   
10. Document processes workflows and configurations to facilitate knowledge sharing and onboarding of new team members.
    

Qualifications:

1. Bachelors or Masters degree in Computer Science Engineering Mathematics or related field.
   
2. Proven experience (4 years) in deploying and managing machine learning models in production environments.
   
3. Strong programming skills in languages such as Python Java or Scala.
   
4. Experience with machine learning frameworks and libraries (e.g. TensorFlow PyTorch scikitlearn).
   
5. Proficiency in containerization technologies such as Docker and orchestration tools like Kubernetes.
   
6. Solid understanding of cloud computing platforms (e.g. AWS Azure Google Cloud) and experience with cloudbased ML services.
   
7. Familiarity with version control systems (e.g. Git) and collaboration tools (e.g. Jira Confluence).
   
8. Knowledge of software development methodologies and best practices (e.g. Agile DevOps).
   
9. Excellent problemsolving and communication skills.
   
10. Ability to work effectively in a fastpaced dynamic environment and collaborate with crossfunctional teams.