Role overview
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'll work on
- 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're looking for
- 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
Tags & focus areas
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