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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Expert RAG Engineer - **Company:** EVENTO LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** XML Schema, Artificial Intelligence, Amazon Web Services, Data Analysis, Computer Vision, Microsoft Azure, Cloud Computing, Nvidia CUDA, Continuous Integration, Linux, DevOps, Github, Information Retrieval, Python (Programming Language), Open Source Technology, Performance Tuning, Scrum Methodology, Salesforce.Com, Systems Integration, Extensible Markup Language (XML), Enterprise Search, Qdrant, Retrieval-Augmented Generation, Large Language Models, Caching, Indexer, Backend, Discretization, GGUF, Pgvector, AI Platforms, Gitlab-ci, Semi-structured Data, Kubernetes, Low Latency, Weaviate, Apache Kafka, Milvus, Ollama, Graph RAG, Model Context Protocol, Docker - **Published:** November 6, 2026 - **Apply:** https://www.builtincolorado.com/auth/login?destination=/job/expert-rag-engineer/12102934 ## About the Role * Experience & Core RAG Expertise: 6+ years in backend, ML, or infrastructure with 2+ years building production RAG systems, including end-to-end architecture, hybrid retrieval, reranking, chunking, indexing, and evaluation frameworks. * Python & Vector Databases: Expert-level Python programming combined with deep knowledge of embeddings, vector databases, and metrics-driven evaluation. * LLM Optimization & Serving: Hands-on experience serving and optimizing open-source LLMs (Ollama, llama.cpp) with quantization, alongside GPU/CUDA troubleshooting and performance tuning. * DevOps, Infrastructure & MCP: Strong proficiency in Microsoft Azure, AWS, Docker, Kubernetes, Linux, CI/CD pipelines (Azure DevOps preferred), and Model Context Protocol (MCP). * Leadership & Collaboration: Strong communication, technical leadership, and ownership to define architectural standards and collaborate effectively in a distributed, remote environment. ## Description This is a senior, hands-on role. You'll define RAG architecture standards, make the key technical decisions on retrieval quality, latency, and cost, and provide technical guidance to AI Developers on the team. You'll work closely with engineering, product, and client stakeholders to turn complex knowledge sources into reliable, measurable, and secure AI features., RAG Architecture & Engineering (core focus) * Design and own end-to-end RAG architectures, from document ingestion to grounded, cited responses. * Build advanced retrieval strategies: hybrid search (dense + BM25/sparse), reranking (cross-encoders), query rewriting/expansion, metadata filtering, and multi-step or agentic retrieval. * Define chunking and indexing strategies for diverse sources, including structured and semi-structured data (XML/XSD, Office formats, PDFs, scanned documents). * Select, benchmark, and fine-tune embedding models; manage vector databases (pgvector, Qdrant, Weaviate, Milvus, Azure AI Search, or similar) at scale. * Build evaluation frameworks for retrieval and generation quality (recall@k, MRR, nDCG, faithfulness, answer relevance) and run them continuously as part of CI/CD. * Reduce hallucinations through grounding, citation, guardrails, and confidence handling. * Implement permission-aware and multi-tenant retrieval so users only access data they're authorized to see. * Develop MCP (Model Context Protocol) integrations that expose RAG systems and internal data sources to LLMs and agents. * Optimize LLM serving with Ollama and llama.cpp: quantization (GGUF), GPU/CUDA tuning, batching, caching, and offline/air-gapped deployments. Technical Leadership * Set RAG architecture standards, reference implementations, and best practices for the AI department. * Review designs and code, and provide technical guidance to AI Developers. * Translate client and product requirements into retrieval strategies with clear quality, latency, and cost targets. * Evaluate emerging techniques (GraphRAG, long-context vs. retrieval trade-offs, agentic RAG) and recommend what's worth adopting. DevOps & Infrastructure (supporting) * Deploy and scale RAG services on Microsoft Azure and AWS using Docker and Kubernetes. * Maintain CI/CD pipelines for AI services (Azure DevOps preferred; GitHub Actions / GitLab CI also used). * Build observability for RAG systems: tracing, retrieval diagnostics, latency, cost, and quality dashboards. * Support event-driven ingestion and re-indexing pipelines using Apache Kafka. * Troubleshoot across the stack - dig into root causes rather than patching symptoms., Remote Entry level Entry level Events * Food * Travel * Hospitality * Hotels & Resorts Coordinates hotel events from planning through execution by supporting client communications and sales activities, managing event logistics, updating booking systems, arranging VIP services, and preparing event documents such as BEOs, group resumes, menus, diagrams, and post-event reports. 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Responsibilities include coaching and mentoring sales staff, supporting deal progression and negotiations, coordinating with Marketing, Account Development, Sales Engineering, Finance, and Order Operations, driving market-growth initiatives, embedding company values, and hiring and developing a high-performing team. Top Skills: Salesforce What you need to know about the Colorado Tech Scene With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation. 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