Summary: Design technical infrastructure for AI solutions.
Responsibilities:
- Create blueprints for ML pipelines, data ingestion, and APIs.
- Optimize systems for performance, security, and cost.
- Select frameworks (e.g., TensorFlow, PyTorch) and tools.
Skills: - Proficiency in cloud AI services and microservices architecture.
Key Process: Technical Design of AI Systems
- Inputs: Solution requirements, scalability needs, security standards.
- Activities:
- Design microservices for model deployment.
- Select frameworks (e.g., TensorFlow Serving).
- Optimize latency/cost for inference pipelines.
- Outputs: Technical specifications, API designs, infrastructure diagrams.
- Stakeholders: Software engineers, DevOps, cybersecurity.
- Tools: Kubernetes, FastAPI, PyTorch.