Job Description:
Key Responsibilities
- Application Development: Code and integrate LLM APIs (OpenAI, Anthropic, etc.) or local models into backend services using Python, FastAPI, etc., OpenTelemetry logging.
- MCP Server Development: Design and implement custom MCP servers using the official SDKs (Python/TypeScript) to expose internal databases, APIs, and file systems to AI agents.
- RAG Implementation: Build & validate RAG—specifically coding the data ingestion scripts, text chunking logic for optimal retrieval.
- Agent Development: Implement multi-step workflows using LangChain, LangGraph, CrewAI etc., focusing on tool-calling logic and error handling.
- Evaluation & Guardrails: Validate & finetune the solution you developed (correctness, latency, token optimization, hallucination, bias etc.,). Coverage of common GenAI guardrails.
- Data Preparation: Prepare/finetune input data as needed.
Technical Skills (The "Execution" Stack)
- Language: Advanced Python (Asyncio, Pydantic) and optional TypeScript/Node.js (for full-stack integration).
- AI Frameworks: Hands-on experience with any of LangChain, LlamaIndex, and Hugging Face Transformers. RAG and Vector search concepts.
- Data Handling: Proficiency in SQL and handling other formats (PDFs, Markdown, JSON).
- Local Validation: Docker, Experience as an end-user of CI pipelines to ensure compliance to quality.
- MCP/API Proficiency: Deep understanding of RESTful APIs, Streaming HTTP, MCP server vs client, JSONRPC
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