出海雷达

AI工程师

AI Developer

AI开发工程限定地区（需当地身份）与中国几乎无重叠，需长期倒时差
公司Salvo Software
薪资未公开
工作地点Mexico
地域资格限定地区（需当地身份）
时区要求与中国几乎无重叠，需长期倒时差
用工类型Full Time
发布时间9 天前
数据来源Himalayas
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注意地域限制：该职位明确限定在 Mexico 招聘。如果你是位于中国大陆的求职者，通常需要当地工作身份才能投递，或需与雇主确认是否接受独立合同（Contractor）形式合作。
作息提示：与中国几乎无重叠，需长期倒时差。
关于本页中文：职位描述由本机离线模型翻译，属机器翻译，供快速判断用；正式沟通与投递请以英文原文为准。官方翻译通道恢复后会自动重译升级。

关于 Salvo 软件，Salvo 软体是一家全球公司，提供经济高效的软件解决方案来指导企业和初创企业通过数字化转型。我们与美国，拉塔姆和印度各地分布式团队合作，建立出高性能，可扩展系统，以解决复杂技术挑战。我们的文化重视创新，拥有权和工程卓越性。角色概述 我们正在寻找一个具有强大的后端和机器学习工程背景的人才开发人员，以便在本地和离线环境中设计，培训，优化和部署 LLM 模型。这一角色是深入技术和实践性，需要在 Python ML 堆中专业知识，模型优化，本地推理框架，RAG （恢复增强代）架构，MCP （模式语境协议）集成以及为线下系统定制的 DevOps 工作流程。你将与我们的工程和产品团队密切合作建立端到端 LLM 管道包括数据预处理，监督细节调整，模型量化，评估，RAGAI 管道设计和使用本地或空隙基础设施部署。如果你喜欢与尖端开源 LLMs 一起工作，构建文脈意识的系统，并设计可靠后端管道，这是适合你的。主要责任：开发核心 LLM · 训练和调整通过监督细节调整 (SFT) 的 LLM. · 与 LLaMA,Mistral,Qwen 等开源模型合作。· 建立 LoRA / Q-LoRA 管道以实现高效的细节调控。·实施并优化数据预处理工作流程，包括代码化和长文本处理。· 使用和扩大 Hugging 面孔变换器和数据集进行培训和推断。·分析和处理结构化和半结构化的数据，包括 XML/XSD 文件。·为 Office 格式 (pythondocx,OpenXML) 实现文件解析解决方案。RAG & Context-Aware Systems · 设计和实施端到端检索增强生成 (RAG) 管道，以基于文档的问题答复和知识检索。· 使用 FAISS,Chroma,Weaviate 或 pgvector 等工具构建并维护向量存储器和嵌入管道。·优化包括针对特定领域的机构定制的混合搜索，重新排名和分类方法在内的检索策略。·开发和维护 MCP 服务器集成，使 LLM 与工具，API 和外部数据进行动态互动。

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About Salvo Software

Salvo Software is a global firm that provides cost-effective software solutions to guide enterprises and startups through digital transformation. With distributed teams across the US, LATAM, and India, we partner with clients to build high-performance, scalable systems that solve complex technical challenges. Our culture values innovation, ownership, and engineering excellence.
Role Overview
We are seeking a highly skilled AI Developer with a strong backend and machine learning engineering background to design, train, optimize, and deploy LLM models in on-prem and offline environments. This role is deeply technical and hands-on, requiring expertise across Python ML stacks, model optimization, local inference frameworks, RAG (Retrieval-Augmented Generation) architectures, MCP (Model Context Protocol) integrations, and DevOps workflows tailored for offline systems.
You will work closely with our engineering and product teams to build end-to-end LLM pipelines — including data preprocessing, supervised fine-tuning, model quantization, evaluation, RAG pipeline design, and deployment using local or air-gapped infrastructure. If you enjoy working with cutting-edge open-source LLMs, building context-aware AI systems, and designing reliable backend pipelines, this role is for you.
Key Responsibilities
Core LLM Development

  • Train and fine-tune LLMs using supervised fine-tuning (SFT).
  • Work with open-source models such as LLaMA, Mistral, Qwen, and similar architectures.
  • Build LoRA / Q-LoRA pipelines for efficient fine-tuning.
  • Implement and optimize data preprocessing workflows, including tokenization and long-context handling.
  • Use and extend Hugging Face Transformers & Datasets for training and inference.
  • Parse and process structured and semi-structured data, including XML/XSD files.
  • Implement document parsing solutions for Office formats (python-docx, OpenXML).

RAG & Context-Aware Systems

  • Design and implement end-to-end Retrieval-Augmented Generation (RAG) pipelines for document-grounded question answering and knowledge retrieval.
  • Build and maintain vector stores and embedding pipelines using tools such as FAISS, Chroma, Weaviate, or pgvector.
  • Optimize retrieval strategies including hybrid search, re-ranking, and chunking approaches tailored for domain-specific corpora.
  • Develop and maintain MCP (Model Context Protocol) server integrations to enable LLMs to interact dynamically with tools, APIs, and external data sources.
  • Design agentic workflows that leverage MCP to give models structured access to internal systems and context in a controlled, auditable manner.

Offline / On-Prem Model Expertise

  • Deploy, run, and maintain models fully offline and in air-gapped environments.
  • Perform model optimization and quantization (GGUF, GPTQ, AWQ, bitsandbytes).
  • Build and maintain inference systems using frameworks like vLLM, TGI, and Ollama.
  • Optimize GPU usage (CUDA, cuDNN, VRAM-aware batching).
  • Maintain local CI/CD pipelines for ML models without cloud dependencies.
  • Manage local model registries, versioning, and artifacts.
  • Ensure RAG and MCP components are fully operational in offline and restricted network environments.

Backend & DevOps

  • Build backend services in Python for ML training and inference workflows.
  • Work with relational databases (Postgres/MySQL) and vector databases for RAG storage layers.
  • Use Docker and Git for reliable development and deployment pipelines.
  • Use Azure DevOps for CI/CD, including local runners when applicable.

Requirements
Technical Skills

  • Strong experience in Python for backend and ML development.
  • Expertise with ML frameworks such as PyTorch or TensorFlow, scikit-learn, and pandas.
  • Solid knowledge of Postgres or MySQL for data storage.
  • Experience with Docker, Git, and DevOps best practices.
  • Hands-on expertise with LLM training, fine-tuning, and optimization.
  • Experience with Hugging Face Transformers & Datasets.
  • Familiarity with XML/XSD and Office document parsing tools.
  • Experience deploying models with vLLM, TGI, or Ollama.
  • Understanding of quantization techniques (GGUF/GPTQ/AWQ).
  • Experience working with GPU optimization and the CUDA stack.
  • Ability to build solutions for offline, on-prem, and air-gapped environments.
  • Hands-on experience designing and implementing RAG pipelines, including embedding models, vector stores (FAISS, Chroma, Weaviate, or pgvector), and retrieval optimization strategies.
  • Experience building or integrating MCP (Model Context Protocol) servers to connect LLMs with external tools, APIs, and structured data sources.

Nice to Have

  • Experience building agentic systems using MCP in production or near-production environments.
  • Familiarity with advanced RAG techniques such as HyDE, re-ranking, or multi-hop retrieval.
  • Experience managing ML model registries in offline environments.
  • Familiarity with AWS for hybrid deployments.
  • Experience with secure environments, restricted networks, or enterprise compliance requirements.

Soft Skills

  • Strong ownership mindset and problem-solving ability.
  • Ability to work effectively in distributed teams across time zones.
  • Clear communication when discussing complex technical topics with both technical and non-technical stakeholders.

Originally posted on Himalayas

本页面信息整理自 Himalayas，版权归原发布方所有。职位可能随时关闭，投递请以原始页面为准。 本站只做信息聚合展示，不参与招聘流程，也不向求职者收取任何费用。

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