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Niche LangChain/LangGraph requirements reduce applicants, but junior AI roles and remote flexibility increase competition.
Core ML and backend skills transfer across industries, but LLM-specific tool requirements add moderate specialization.
Multiple mandatory technical requirements (LangChain, LangGraph, Docker) and explicit 1-3 years make filters stringent.
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Design, develop, and integrate AI-driven backend solutions using Python and ML libraries such as TensorFlow, Keras, and LangChain.
Deploy AI use cases into production with REST APIs and microservices, focusing on scalability and performance.
Collaborate cross-functionally to translate business needs into AI capabilities and maintain AI systems with continuous improvements.
1-3 years professional experience as AI/ML Engineer with strong Python backend development.
Hands-on experience with LangChain and LangGraph in real-world projects.
Practical experience containerizing and deploying applications using Docker.
Proficiency with AI/ML libraries (NumPy, Pandas, TensorFlow, Keras) and AI platforms/APIs like OpenAI or Hugging Face.
Experienced in building and deploying AI/ML applications beyond just model training, focusing on real-world product integration.
Comfortable working independently in a remote setup with ownership of features from concept to production.
Capable of quick prototyping, experimentation, and iteration with AI technologies and workflows.