Position Details
About this role
This role involves designing and maintaining machine learning infrastructure, managing GPU workloads on Kubernetes, and supporting AI/ML model deployment in a cloud-native environment, primarily for healthcare and insurance applications.
Key Responsibilities
- Build ML pipelines
- Manage GPU workloads
- Maintain distributed systems
- Collaborate with data teams
- Ensure system performance
Technical Overview
Focuses on ML pipelines, distributed data systems, container orchestration with Kubernetes and Docker, GPU management, and cloud-native AI/ML deployment using Python, PyTorch, and TensorFlow.
Ideal Candidate
The ideal candidate is a mid-level ML Ops engineer with over 5 years of experience in building and maintaining machine learning pipelines, proficient in Python, and experienced with Kubernetes, Docker, and GPU workloads. They possess strong problem-solving skills and can work independently in a cloud-native environment.
Must-Have Skills
Nice-to-Have Skills
Tools & Platforms
Required Skills
Hard Skills
Soft Skills
Industry & Role
Clearance & Visa
Keywords for Your Resume
Deal Breakers
Less than 5 years of experience, No experience with Kubernetes or GPU workloads, Lack of Python or ML pipeline experience, No active security clearance
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