Application Implementation : Code and integrate LLM APIs (OpenAI, Anthropic, etc.) or local models into backend services using Python, FastAPI, etc.,
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 and maintain the "plumbing" for Retrieval-Augmented Generation—specifically coding the data ingestion scripts, text chunking logic, and metadata filtering.
Vector DB Management : Perform day-to-day operations on vector databases (Pinecone, Milvus, etc.), including indexing, querying, and optimizing search retrieval.
Prompt Programming : Develop, version-control, and refine complex prompt templates (using Jinja2 or similar) to ensure consistent structured outputs (JSON/YAML).
Agent Development : Implement multi-step workflows using LangChain, LangGraph, CrewAI etc., , focusing on tool-calling logic and error handling.
Evaluation & Testing : Build automated test suites to detect "hallucinations" and measure accuracy using frameworks.
Performance Tuning : Implement caching layers and streaming responses to reduce latency and improve the end-user experience; Token optimization.
Data Pre-processing : Clean and tokenize datasets for model fine-tuning or high-quality context retrieval.
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 unstructured data formats (PDFs, Markdown, JSON).
Deployment : Practical experience with Docker , GitHub Actions (CI/CD), and experience with OpenTelemetry, LangSmith, Weights & Biases etc., Understanding of evaluation/guardrails.
MCP/API Proficiency : Deep understanding of RESTful APIs, Streaming HTTP, MCP server vs client, JSONRPC
