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Metro role with popular AI title but specialized LLM/inference skills, moderate candidate competition.
Core LLM, inference and deployment skills transfer across industries, though education-domain familiarity is beneficial.
Explicit 1–3 years plus many mandatory LLM, inference, and deployment skills makes filters strict.
Own design, development, deployment, and monitoring of LLM-based AI features serving student-facing products and internal automation.
Build and maintain retrieval-augmented generation (RAG) pipelines, agentic workflows using LangChain or equivalents, and multimodal integrations (speech and vision).
Manage full AI feature stack: data engineering, model inference, fine-tuning, system design, and reliability for scalable production use.
1–3 years of hands-on AI engineering experience, especially with LLM and RAG systems.
Strong production-level Python skills including async, typing, and use of frameworks like FastAPI.
Experience with vector databases (pgvector, Qdrant, Milvus), SQL (PostgreSQL/MySQL), and agent frameworks (LangChain, LangGraph).
Experience deploying open-weight models with tools like vLLM, Transformers, or llama.cpp.
Operates with a bias for shipping working prototypes rapidly and iterating based on measured evaluations.
Skilled at bridging technical AI complexities with non-technical stakeholders in education domain.
Experienced in end-to-end AI system design including prompt injection defenses, production observability, and cost/latency optimization.