We are looking for an Expert RAG Engineer to design, build, and own the retrieval-augmented generation systems behind our AI products. Your core work will be architecting production-grade RAG pipelines end to end: ingestion, chunking, embeddings, hybrid retrieval, reranking, evaluation, and serving on top of open-source LLMs (Ollama, llama.cpp) in cloud, on-prem, and air-gapped environments.

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.

Key Responsibilities
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

Requirements

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

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📌 Expert RAG Engineer (Ejido de la Finca)
🏢 Salvo Software
📍 Ejido de la Finca