Senior AI Engineer (LLMs & Generative AI)

At a glance on RemoteAI

• 293 reviewed professionals on RemoteAI match this role at 70% or more.
• Python: 2,161 of 3,363 reviewed AI professionals on RemoteAI list it (64%) — the most common skill this role asks for.
• Prompt Engineering: 686 of 3,363 reviewed AI professionals on RemoteAI list it (20%).
• RAG: 568 of 3,363 reviewed AI professionals on RemoteAI list it (17%).
• LangChain: 520 of 3,363 reviewed AI professionals on RemoteAI list it (15%).

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About the role

Responsibilities

• Build and deploy agentic LLM systems with tool calling, function calling, and multi-step workflows for grant discovery and document parsing.
• Own end-to-end RAG pipelines including ingestion, chunking, hybrid retrieval, reranking, and grounded citations.
• Select and manage embedding models and vector stores like pgvector, Qdrant, and Pinecone while monitoring drift.
• Collaborate with Product and Design, run A/B experiments, and contribute to alerting and incident response.
• Write clean, tested, documented code with cost and latency optimization in mind.

Requirements

• 10+ years software engineering with 8+ years focused on production LLMs.
• Proven experience building agentic tool calling workflows and planning loops using LangChain, LangGraph, or LlamaIndex.
• Strong RAG expertise across ingestion, embeddings, hybrid search, reranking, and grounding.
• Hands-on with vector stores including pgvector, Qdrant, Pinecone, Weaviate, and Milvus.
• Experience designing eval suites for RAG, QA, and summarization using Ragas, DeepEval, or OpenAI Evals.
• Proficient in Python, FastAPI, Celery, GCP/AWS, Docker, CI/CD, and observability tooling.
• Comfortable with SQL, schema design, and data pipeline development.
• Proficiency in English communication

Benefits

• Fully Remote at anywhere
• $3000 annual learning and conference budget
• Long-term contract
• Diverse client engagements in AI infrastructure

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