AI Product Interview Prep 02: What skillsets does software engineer in API require?

Besides software engineering in inference positions, API product involve more application-level features. We can dive deeper to find commonalities across these types of software engineer positions.

Programming Languages & Frameworks

• Python: Mentioned in almost all positions, especially at Anthropic and Perplexity

• TypeScript/JavaScript: For Node.js SDKs and frontend integration

• Go: Mentioned for OpenAI's SDK support

• FastAPI: Specifically mentioned at Anthropic for backend API development

• React: For frontend components that interact with APIs

Database Technologies

• PostgreSQL/AlloyDB: Primary relational database

• DynamoDB: NoSQL database mentioned at Perplexity

• Redis: Caching layer mentioned at Perplexity

• Vector Stores: For AI/ML model data storage

Infrastructure & DevOps

• Kubernetes: Container orchestration

• Cloud Platforms: GCP (Anthropic), AWS (implied at Perplexity)

• Cloud Run: Serverless compute at Anthropic

• Infrastructure as Code: Mentioned for ML Systems Engineer role

• CI/CD Pipelines: For automated testing and deployment

AI/ML Related Skills

• PyTorch: For ML model integration

• GPU/ML acceleration hardware: For model inference

• LLM/NLP understanding: Working with language models

• Model evaluation frameworks: For testing AI capabilities

API Design & Development

• RESTful API design principles

• API documentation standards

• SDK development and maintenance

• API versioning strategies

Monitoring & Observability

• Prometheus & Grafana: For monitoring and visualization

• Logging systems: For debugging and issue tracking

• Performance optimization: For high-throughput APIs

General Software Engineering

• SQL proficiency: For data analysis and querying

• Distributed systems design: For scalable APIs

• High-throughput services: For handling large volumes of requests