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Metro, mid-level AI role with popular LLM skills and some niche tooling, medium competition.
Requires specialized LLM and vector-search expertise, making backgrounds less transferable across unrelated industries.
Multiple explicit filters: 2–4 years, 1+ year LLM experience, specific frameworks, and timezone overlap.
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Build and maintain agentic AI pipelines, retrieval infrastructure, and integrations with real component ownership under senior engineer guidance.
Implement and tune retrieval-augmented generation (RAG) pipelines including embedding, chunking, vector retrieval, and evaluation.
Collaborate with data engineers and domain experts to integrate AI components with upstream data and downstream applications while maintaining clean, well-tested Python code.
2–4 years of software or ML engineering experience, with at least 1 year working with large language models (LLMs) or AI systems professionally.
Working knowledge of LLM APIs (e.g., OpenAI, Anthropic) and at least one agentic or RAG framework (e.g., LangChain, LlamaIndex).
Proficient in Python with experience in software engineering practices like version control, testing, and REST APIs.
Ability to overlap Pacific Time business hours for a minimum of 3 hours daily.
Experienced in end-to-end development and tuning of advanced AI systems, especially retrieval-augmented generation and vector database integration.
Operates effectively in a cross-functional, client-facing environment with clear communication and collaboration skills.
Comfortable working under guidance but with real ownership and autonomy over AI system components in a fast-paced, global engineering team.