Senior Engineer AI
Tasks
- Architect RAG systems using vector databases and embedding models
- Build agentic workflows using tool function calling and structured output parsing
- Build evaluation frameworks for accuracy relevance hallucination and regression
- Contribute to architecture decisions for model hosting and build vs buy
- Deploy and manage AI services on Kubernetes with CI CD pipelines
- Design build and maintain production AI applications end to end
- Develop backends using message queues caching and relational databases
- Implement function calling and structured JSON XML output handling
- Integrate AI capabilities with third party platforms
- Optimize inference cost using caching token budgeting and batching
- Use observability tools for AI service monitoring
- Write and iterate system prompts and few shot examples
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Anthropic | CI/CD | Caching | Chunking | Docker | Embeddings | Evaluation | FastAPI | Few-Shot Learning | Few-shot | Flask | Function Calling | GCP | JSON | JavaScript | Kubernetes | Litellm | Model routing | Next.js | Observability | OpenAI | OpenRouter | Opik | PostgreSQL | Prompt Compression | Prompt engineering | Python | RAG | RabbitMQ | Redis | Sentry | Structured Output | System prompts | Token Usage Monitoring | Token budgeting | Token usage | Tool use | TypeScript | Usage monitoring | Vector Databases | XML
Education
N/A
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