What you'll do
- Design and build production-grade GenAI applications using LangChain, LangGraph, and CrewAI
- Implement RAG pipelines with vector databases (Pinecone, Weaviate, ChromaDB) for enterprise knowledge systems
- Fine-tune and deploy LLMs (GPT-4, Claude, Llama) for domain-specific use cases
- Build multi-agent orchestration systems for complex workflow automation
- Develop evaluation frameworks to benchmark model performance, latency, and cost
What you'll need
- 2+ years working with LLMs, prompt engineering, or NLP in production environments
- Strong Python skills — comfortable with FastAPI, asyncio, and data pipelines
- Experience with at least one agent framework: LangChain, LangGraph, CrewAI, or AutoGen
- Understanding of embedding models, vector search, and retrieval-augmented generation
- Bonus: experience with PyTorch, model fine-tuning, or MLOps (MLflow, Weights & Biases)
PythonLangChainOpenAIRAGLLMsFastAPI
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