About expert.ai

We build production-grade AI systems for enterprise clients. Our work focuses on Large Language Models, Retrieval-Augmented Generation, agentic architectures, and knowledge-driven AI. We are a lean team of engineers and researchers who move fast and care about the quality of what we ship.

The Role

We are looking for an AI Engineer with around 2 to 4 years of experience — someone past the learning phase, who has shipped at least one LLM-powered system to production and knows what breaks, what scales, and what was a bad idea in hindsight.

You do not need to have done everything. You need to have done some things well, understand why they worked, and be ready to go deeper.

What You Will Do

Design and implement RAG pipelines end-to-end: ingestion, chunking strategies, embedding models, vector retrieval, reranking, and response generation
Build agentic workflows using LangChain, LangGraph, LlamaIndex, or custom orchestration — including tool use, memory management, and multi-step reasoning
Integrate and prompt-engineer LLMs (GP Claude Sonnet/Opus, Qwen) for domain-specific tasks; contribute to fine-tuning efforts when needed
Develop MCP servers and clients to standardize tool and context exposure across AI systems
Maintain vector databases (FAISS, Pinecone, Weaviate, Qdrant, pgvector) and optimize retrieval quality
Build evaluation pipelines to track hallucination rate, retrieval precision, latency, and output consistency over time
Write clean, tested, production-ready Python and contribute to code reviews
Collaborate with senior engineers and clients to translate requirements into solid technical decisions

What We Expect

2 to 4 years of software engineering experience, with at least 1 to 2 years focused on LLM or applied AI systems
At least one production RAG or agentic application under your belt — you know what it took to get it there
Solid understanding of embeddings, transformer fundamentals, context management, and prompt design patterns
Familiarity with Docker
Able to work with autonomy — you ask good questions, but you do not wait to be told what to do next

Formal degrees are welcome but not required. What matters is what you have shipped.

Nice to Have

Experience with MCP protocols in real projects
Knowledge graphs or GraphRAG pipelines (Neo4j, Neptune, or similar)
Inference optimization: quantization (GGUF, AWQ, GPTQ), vLLM
Evaluation tooling: RAGAS, TruLens, or custom eval design
Domain NLP experience in legal, manifacturing, or healthcare