Key Responsibilities
Design and develop scalable AI applications using Python, LangChain, and LangGraph.
Build and optimize Retrieval-Augmented Generation (RAG) solutions using vector databases.
Develop AI agents, multi-agent systems, and workflow orchestration using LangGraph.
Integrate Large Language Models (LLMs) such as OpenAI GPT, Azure OpenAI, Claude, Gemini, or Llama.
Create prompt engineering strategies to improve model performance and accuracy.
Develop REST APIs and backend services for AI applications.
Implement memory management, tool calling, agent execution, and workflow automation.
Work with structured and unstructured data sources for AI-based solutions.
Monitor, troubleshoot, and optimize GenAI application performance.
Collaborate with cross-functional teams to deliver enterprise-grade AI solutions.
Required Skills
Technical Skills
Solid proficiency in Python Programming.
Hands-on experience with LangChain and LangGraph.
Experience with Generative AI and Large Language Models (LLMs).
Knowledge of Prompt Engineering techniques.
Experience in building RAG (Retrieval-Augmented Generation) solutions.
Hands-on experience with Vector Databases such as Pinecone, FAISS, ChromaDB, Weaviate, or Milvus.
Knowledge of Embeddings and Semantic Search.
Experience with REST APIs using Flask or FastAPI.
Robust understanding of AI application deployment and MLOps concepts.
Experience with Git and CI/CD pipelines.