AI Engineer (LLM/RAG)

Location: Remote US

Company

hirify.global uses AI to uncover business logic and technical insights in legacy code, helping enterprises modernize and maintain applications more safely.

What you will do

• Design, implement, and optimize LLM-powered systems, including RAG pipelines, chat agents, summarizers, and knowledge graph integrations.
• Build data indexing and retrieval pipelines with LlamaIndex, LangChain, or similar frameworks.
• Implement and maintain vector database solutions using technologies such as Pinecone, Neo4j, FAISS, Milvus, Weaviate, Chroma, or Azure Cognitive Search.
• Integrate open-source and proprietary LLMs into the hirify.global Platform and develop generative insights, automated summarization, and narrative analytics.
• Collaborate with product, data, infrastructure, DevOps, and backend teams to prototype and deploy scalable AI services.
• Benchmark model performance, latency, and cost while contributing to documentation, experimentation frameworks, and evaluation methodologies.

Requirements

• 7+ years of overall engineering experience, including at least 3+ years in AI engineering, machine learning, or applied NLP.
• Hands-on experience with LlamaIndex, LangChain, or similar AI orchestration frameworks.
• Experience designing and implementing vector database solutions.
• Strong understanding of LLM APIs, retrieval-augmented generation, embeddings, and tokenization.
• Proficiency in Python and experience with FastAPI, Pandas, or NumPy.
• Familiarity with prompt engineering, tool calling, chat agent architectures, performance optimization, and scalable systems.

Nice to have

• Production deployment experience using Docker, Azure, or AWS.
• Exposure to LangGraph, semantic search, or hybrid RAG systems.
• Familiarity with knowledge graphs, document intelligence, or multimodal AI.
• Experience in SaaS or early-stage startup environments.

Culture & Benefits

• Flexible, remote-first work environment.
• Competitive compensation and equity.
• Opportunity to define and build the AI roadmap.
• Collaborative, learning-oriented culture.
• Access to advanced AI models, research, and infrastructure.