NLP / LLM Engineer

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Описание

Responsibilities: — Design, build, and improve production NLP/LLM systems, including RAG, search, AI agents, document intelligence, and multilingual NLP. — Build retrieval pipelines involving chunking, embeddings, hybrid search, filtering, reranking, and context construction. — Develop multi-step LLM workflows and agents with routing, tool use, structured outputs, retries, and failure recovery. — Build evaluation pipelines for retrieval and generation quality. — Analyze and reduce hallucinations and improve grounded generation and answer quality. — Create and clean datasets, perform error analysis, generate synthetic data, and apply model adaptation techniques such as SFT, LoRA, QLoRA, and PEFT when appropriate. — Develop production APIs and services using Python and tools such as FastAPI, Docker, Git, automated testing, and CI/CD. — Improve reliability, latency, token usage, inference cost, and overall system performance. — Run controlled experiments, regression tests, A/B tests, and quantitative evaluations. Requirements — 3+ years of professional experience in NLP, Machine Learning, Information Retrieval, Applied AI, or a related field. — Strong Python engineering skills and experience maintaining production software. — Hands-on experience shipping at least one LLM, RAG, NLP, search, or agent-based system into real production use. — Strong understanding of transformers, tokenization, embeddings, context windows, prompting, structured generation, and common LLM failure modes. — Practical understanding of retrieval beyond simply connecting an embedding model to a vector database. — Experience with semantic search, keyword search, hybrid retrieval, filtering, reranking, chunking, and indexing. — Experience with at least one modern LLM ecosystem such as OpenAI, Gemini, Anthropic, Hugging Face, or open-source models. — Experience with PyTorch or another modern deep-learning framework. — Ability to evaluate AI systems quantitatively rather than relying only on manual

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