Expert RAG Engineer

Salvo Software; México; Teletrabalho; MXN 700 000 - 1 200 000; Tempo integral

Resumo da oferta

Salvo Software LLC in Mexico invites an Expert RAG Engineer to design, build, and own retrieval-augmented generation systems for our AI products. Lead end-to-end pipelines, from ingestion to grounded, cited responses, across cloud, on‑prem, and air‑gapped environments.

You'll set architecture standards, optimize retrieval quality, latency, and cost, and mentor AI developers while collaborating with product and client teams to deliver reliable, secure features.

Qualificações

• 6+ years in backend/ML or infra with 2+ years building production RAG systems.
• Expert Python programming with embeddings and vector databases.
• Hands-on experience with open-source LLMs (Ollama, llama.cpp).
• Cloud and on-prem deployments, with multi-tenant considerations.
• Strong leadership and cross-team collaboration abilities.

Responsabilidades

• Design end-to-end RAG architectures from ingestion to grounded responses.
• Build advanced retrieval strategies and evaluation frameworks.
• Deploy and scale RAG services on Azure and AWS using Docker & Kubernetes.
• Maintain CI/CD pipelines and observability dashboards for RAG systems.
• Implement multi-tenant retrieval and MCP integrations with LLMs.
• Guide AI developers with architectural decisions and best practices.

Conhecimentos

Python

Vector databases

LLM optimization

DevOps

Leadership

Ferramentas

Docker

Kubernetes

Linux

Azure DevOps

CI/CD

Descrição da oferta de emprego

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