Expert RAG Engineer

🔥 0 minutes ago

🌐 Mexico, United States – Remote

🇲🇽 Mexico – Remote 🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷🏻‍♀️ Engineer

👻 Ghost score 15%

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Salvo Software

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤝 B2B

☁️ SaaS

🏢 Enterprise

B2B • SaaS • Enterprise

Salvo Software is a global software development company headquartered in Vancouver, WA, with near-shoring capabilities that offer a blend of international pricing and U. S. standards. The company specializes in custom software solutions, providing services such as software product development, AI-enabled applications, blockchain development, IoT, speech recognition, web application development, and business automation. Salvo Software is dedicated to helping enterprises and startups develop strategic products through agile development processes and adaptable, affordable services. Their expertise includes UI/UX design, QA and testing, embedded firmware development, and cloud application development, supporting various industries with tailored solutions like e-commerce platforms, CRM systems, and educational resource platforms.

📋 Description

• Design and own end-to-end RAG architectures from document ingestion to grounded, cited responses • Build advanced retrieval strategies including hybrid search, reranking, query rewriting/expansion, metadata filtering, and multi-step or agentic retrieval • Define chunking and indexing strategies for structured and semi-structured sources, including XML/XSD, Office formats, PDFs, and scanned documents • Select, benchmark, and fine-tune embedding models • Manage vector databases such as pgvector, Qdrant, Weaviate, Milvus, and Azure AI Search at scale • Build continuous evaluation frameworks for retrieval and generation quality • Reduce hallucinations through grounding, citation, guardrails, and confidence handling • Implement permission-aware and multi-tenant retrieval • Develop MCP integrations exposing RAG systems and internal data sources to LLMs and agents • Optimize LLM serving with Ollama and llama.cpp, including GGUF quantization, GPU/CUDA tuning, batching, caching, and offline/air-gapped deployments • Set RAG architecture standards, reference implementations, and best practices • Review designs and code and provide technical guidance to AI Developers • Translate client and product requirements into retrieval strategies with quality, latency, and cost targets • Evaluate emerging techniques such as GraphRAG, long-context trade-offs, and agentic RAG • Deploy and scale RAG services on Microsoft Azure and AWS using Docker and Kubernetes • Maintain CI/CD pipelines for AI services • Build observability for tracing, retrieval diagnostics, latency, cost, and quality dashboards • Support event-driven ingestion and re-indexing pipelines using Apache Kafka • Troubleshoot root causes across the stack

🎯 Requirements

• 6+ years in backend, ML, or infrastructure • 2+ years building production RAG systems, including end-to-end architecture, hybrid retrieval, reranking, chunking, indexing, and evaluation frameworks • Expert-level Python programming • Deep knowledge of embeddings, vector databases, and metrics-driven evaluation • Hands-on experience serving and optimizing open-source LLMs (Ollama, llama.cpp) with quantization • GPU/CUDA troubleshooting and performance tuning • Strong proficiency in Microsoft Azure, AWS, Docker, Kubernetes, Linux, and CI/CD pipelines (Azure DevOps preferred) • Proficiency with Model Context Protocol (MCP) • Strong communication, technical leadership, and ownership • Ability to collaborate effectively in a distributed, remote environment

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