MLOps Engineer

Overview:

We are looking for an MLOps Engineer with a strong background in operationalizing AI solutions at scale. The ideal candidate will have expertise in developing and managing machine learning lifecycle frameworks and pipelines and integrating these solutions with client systems.

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.