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Popular junior LLM/AI engineering role with broad skill requirements attracts moderate applicant competition.
Core Python, cloud, and LLM engineering skills are broadly transferable across industries.
Explicit 1–3 years plus mandatory Python, LLM, cloud, RAG, and API skills require strict technical fit.
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Design, build, and deploy Generative AI solutions including LLM integrations, RAG pipelines, and AI-driven automation tools.
Develop and maintain Python-based AI applications, REST APIs (FastAPI/Flask), and data processing scripts for structured and unstructured data.
Deploy AI applications on AWS or Azure cloud services, manage containerization (Docker), and support CI/CD pipelines for scalable cloud operations.
1-3 years of experience in software engineering or AI development with strong Python coding skills.
Hands-on experience with large language models (LLMs), RAG techniques, prompt engineering, embeddings, and vector search.
Experience deploying and managing AI solutions on AWS or Azure cloud platforms.
API development experience including building REST APIs; familiarity with JSON integrations.
Has practical experience integrating and deploying AI solutions using frameworks like LangChain, LlamaIndex, or similar and services like Azure OpenAI or AWS Bedrock.
Comfortable working in fast-paced AI development environments requiring cross-team collaboration and version control (Git).
Understands MLOps fundamentals, AI model evaluation, and has exposure to vector databases (FAISS, Pinecone, OpenSearch).