Back
Mid-level metro GenAI role with broad, popular skill requirements increases competition.
Core Python and LLM skills transfer well, but LangChain/RAG enterprise experience adds some domain specificity.
Explicit years plus mandatory GenAI, LangChain, RAG, vector DB and cloud skills make filters stringent.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy Generative AI applications leveraging Large Language Models (LLMs) and AI agents using LangChain and LangGraph.
Develop scalable backend services and REST APIs using Python, integrating AI platforms such as OpenAI, Azure OpenAI, and Anthropic for enterprise use cases.
Implement and optimize prompt engineering, Retrieval-Augmented Generation (RAG) solutions with vector databases, and monitor GenAI application performance and reliability.
4 to 9 years of professional experience, with 4 to 8 years specifically in Python development.
Strong hands-on experience with Generative AI concepts including LLMs, LangChain, LangGraph, and prompt engineering.
Knowledge and experience with vector databases (e.g., Pinecone, ChromaDB, Weaviate, FAISS) and RAG architecture.
Experience in backend development, REST APIs, microservices, version control (Git), CI/CD pipelines, and cloud platforms (Azure, AWS, or GCP).
Proven ability to architect and optimize enterprise-grade Generative AI applications and workflows involving complex AI agent and multi-agent system development.
Experienced in full backend engineering lifecycle integrating advanced AI technologies with scalable API and microservice architectures.
Comfortable working with both structured and unstructured data to design, fine-tune, and monitor AI models for robustness and performance in production environments.