Middle Machine Intelligence Interface Engineer

Middle Machine Intelligence Interface Engineer Negotiable Georgia Tbilisi Full Remote Work experience 3 years 5 months Last work experience VkusVill AI Engineer 3 years 5 months Contacts About About AI Engineer focused on building production-oriented LLM applications, Retrieval-Augmented Generation (RAG) systems, and AI agents. Experienced in Python, LLM integration, information retrieval, vector search, hybrid retrieval, reranking, tool calling, evaluation, and observability. Passionate about turning complex AI concepts into practical, reliable software solutions. Affiliate experience No data available Work experience 3 years 5 months April 2023 - August 2026 ( 3 years 5 months ) VkusVill AI Engineer Building production-grade LLM applications, advanced RAG architectures, and autonomous AI agents. • Co-designed and scaled a production-grade internal RAG assistant (serving 500+ daily active users, cutting employee research time by 40% and processing 15K+ multi-format documents) supporting heterogeneous data parsing, hierarchical chunking, and metadata filtering to optimize semantic context delivery. • Implemented a high-performance retrieval pipeline (dense vector search + BM25 + RRF + Cross-Encoder) that eliminated hallucinations on critical queries and boosted search accuracy from 62% to 89%, directly improving user adoption and retention. • Collaborated closely with product managers, data analysts, and backend engineering teams to align LLM capabilities with business requirements, translating complex user needs into robust technical specifications. • Contributed to the design of an offline evaluation framework with ~800 curated golden queries, utilizing Ragas to decouple retrieval evaluation (Context Precision/Recall) from generation evaluation (Faithfulness, Answer Relevancy) for automated CI/CD regression testing. • Implemented end-to-end LLM observability via Langfuse and OpenTelemetry, tracking P95 latency, token consumption, and failure modes, which drove prompt caching and context pruning strategies reducing inference costs by 30%. • Fine-tuned Llama-3.1-8B-Instruct via Hugging Face and QLoRA on 1.5K instruction examples using custom chat templates, boosting structured-response adherence from 78% to 91% and eliminating manual post-processing/formatting by support agents. • Developed core components of an agentic workflow integrating LLM tool/function calling with internal REST APIs and microservices, orchestrating multi-step execution loops (data retrieval, runtime validation, and action execution) with automatic error recovery and state management. Skills Transformers Hugging Face Prompt Engineering Fine-tuning PEFT LoRA RAG Embeddings Vector Search Qdrant BM25 Hybrid Search RRF Cross-Encoder Reranking Query Rewriting Chunking SQL Python FastAPI PyTorch Language proficiency Intermediate English Native Russian Employment Employment Full Work format Remote Work schedule Flexible, Shift, 5/2 Relocation Possible Business trips Business trips possible