Senior Engineer AI
Tasks
- Architect RAG systems using vector databases embedding models and chunking
- Build agentic workflows with tool function calling structured output parsing
- Build and maintain evaluation frameworks for accuracy relevance hallucination and regression
- Deploy and manage AI services on Kubernetes with CI CD on AWS GCP
- Design build and maintain production AI applications end to end
- Implement function calling tool use patterns and structured JSON XML output handling
- Integrate AI capabilities with third party platforms
- Integrate AI services with message queues caching layers and relational databases
- Optimize inference costs using model selection caching token budgeting and batching
- Use observability tools to monitor production AI quality
- Write and iterate system prompts few shot examples and prompt chains
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | CI/CD | Caching | Chunking | Docker | Embedding Models | FastAPI | Flask | Function Calling | GCP | JSON | Kubernetes | LLM Evaluation | Model routing | Next.js | Node.js | Observability | PostgreSQL | Prompt engineering | Python | RAG | RabbitMQ | Redis | Sentry | Structured Output | System design | Token Compression | Token Usage Monitoring | Token budgeting | Token usage | Tool use | TypeScript | Usage monitoring | Vector Databases | XML
Education
N/A
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