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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

• 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 using python-docx and OpenXML • Design and implement end-to-end RAG pipelines for document-grounded question answering and knowledge retrieval • Build and maintain vector stores and embedding pipelines using FAISS, Chroma, Weaviate, or pgvector • Optimize retrieval strategies including hybrid search, re-ranking, and chunking for domain-specific corpora • Develop and maintain MCP server integrations for LLM access to tools, APIs, and external data sources • Design agentic workflows using MCP to provide controlled, auditable access to internal systems and context • Deploy, run, and maintain models fully offline and in air-gapped environments • Perform model optimization and quantization using GGUF, GPTQ, AWQ, and bitsandbytes • Build and maintain inference systems using vLLM, TGI, and Ollama • Optimize GPU usage with CUDA, cuDNN, and 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 operate in offline and restricted-network environments • Build Python backend services for ML training and inference workflows • Work with relational and vector databases for RAG storage layers • Use Docker and Git for development and deployment pipelines • Use Azure DevOps for CI/CD, including local runners when applicable

🎯 Requirements

• Strong experience in Python for backend and machine learning development • Expertise with PyTorch or TensorFlow, scikit-learn, and pandas • Solid knowledge of Postgres or MySQL • Experience with Docker and Git • Hands-on experience 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 GGUF, GPTQ, or AWQ quantization • Experience with GPU optimization and the CUDA stack • Experience building solutions for offline, on-prem, and air-gapped environments • Hands-on experience designing and implementing RAG pipelines, including embedding models, vector stores, and retrieval optimization • Experience building or integrating MCP servers • Experience with advanced RAG techniques such as HyDE or multi-hop retrieval • Experience discussing complex technical topics with technical and non-technical stakeholders