Description

• Model Deployment: Build APIs and services to serve ML models.  
• Optimization: Improve model inference speed and scalability.  
• Pipelines: Develop automated pipelines for training and deployment.  
• Integration: Embed ML services into production applications.  
• Monitoring: Track model performance and reliability in production.

Qualifications:  

• 7-10 years of software engineering experience, with a focus on machine learning systems

Preferred Qualifications:  

• Strong software engineering background (Python required).  
• Experience with REST APIs and microservices.  
• Familiarity with Docker, Kubernetes, and cloud platforms.  
• Understanding of ML lifecycle and deployment challenges

Key Skills

Education

Any Gradute