Expert RAG Engineer

Salvo Software

. Design and own end-to-end RAG architectures from document ingestion to grounded, cited responses .

Posted 11/6/2026full-timeRemote • Mexico, United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing end-to-end RAG architectures, optimizing LLM serving, and managing vector databases. Proficient in building advanced retrieval strategies and maintaining CI/CD pipelines for AI services.

Highest-signal resume keywords

Expert-Level Python ProgrammingProduction RAG Systems DevelopmentVector Database ManagementMicrosoft Azure and AWS ProficiencyGPU/CUDA Performance Tuning

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Hard Skills

End-to-End RAG ArchitectureHybrid Retrieval StrategiesChunking and IndexingEmbedding Models Fine-TuningContinuous Evaluation FrameworksPermission-Aware RetrievalMCP IntegrationsLLM OptimizationCI/CD Pipeline MaintenanceEvent-Driven Ingestion

Soft Skills

Strong CommunicationTechnical LeadershipOwnershipCollaboration in Remote Environment

Tools & Technologies

DockerKubernetesMicrosoft AzureAWSPgvectorQdrantWeaviateMilvusOllamaLlama.cpp

Industry Keywords

Machine LearningRetrieval-Augmented GenerationMetrics-Driven EvaluationDocument IngestionDistributed Systems

Tech Stack

Tools & technologies

ApacheAWSAzureDockerKafkaKubernetesLinuxPython

About the role

Key responsibilities & impact

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

What you’ll need

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