MLOps Engineer

Key Responsibilities:

- Infrastructure Management: Build scalable and robust infrastructure for ML models, ensuring seamless production integration.

- CI/CD Expertise: Develop and maintain CI/CD pipelines with a focus on ML model deployment.

- Model Deployment and Monitoring: Deploy ML models using TensorFlow Serving, Pytorch Serving, Triton Inference Server, or TensorRT and monitor their performance in production.

- Collaboration: Work closely with data scientists and software engineers to transition ML models from research to production.

- Security and Compliance: Uphold security protocols and ensure regulatory compliance in ML systems.

Skills and Experience Required:

- Proficiency in Docker and Kubernetes for containerization and orchestration.

- Experience with CI/CD pipeline development and maintenance.

- Experience in deploying ML models using TensorFlow Serving, Pytorch Serving, Triton Inference Server, and TensorRT.

- Experience with cloud platforms like AWS, Azure, and GCP.

- Strong problem-solving, communication, and teamwork skills.

Qualifications:

- Bachelor’s/Master’s degree in Computer Science, Engineering, or a related field.

- 4-6 years of experience in ML project management, with a recent focus on MLOps.

Additional Competencies:

- AI Technologies Deployment, Data Engineering, IT Performance, Scalability Testing, and Security Practices.