We design custom-built solutions to help you transform, scale, and grow your business along with a team that cares about you.
Salvo software is a global firm with near-shoring capabilities headquartered in Vancouver, WA. That provides cost-effective software solutions to guide enterprises and startups through digital transformation.
We help our partners to improve their client’s customer experience and optimize their business process times by providing hand-selected teams of experts that meet their needs and help them to make smart decisions.

Salvo Software

We provide affordable custom software solutions specialized in ERP Systems, Business Automation, and Blockchain.

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.

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 architecturesBuild LoRA / Q-LoRA pipelines for efficient fine-tuningImplement and optimize data preprocessing workflows, including tokenization and long-context handlingUse and extend Hugging Face Transformers & Datasets for training and inferenceParse and process structured and semi-structured data, including XML/XSD filesImplement 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 retrievalBuild and maintain vector stores and embedding pipelines using tools such as FAISS, Chroma, Weaviate, or pgvectorOptimize retrieval strategies including hybrid search, re-ranking, and chunking approaches tailored for domain-specific corporaDevelop and maintain MCP (Model Context Protocol) server integrations to enable LLMs to interact dynamically with tools, APIs, and external data sourcesDesign 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 environmentsPerform model optimization and quantization (GGUF, GPTQ, AWQ, bitsandbytes)Build and maintain inference systems using frameworks like vLLM, TGI, and OllamaOptimize GPU usage (CUDA, cuDNN, VRAM-aware batching)Maintain local CI/CD pipelines for ML models without cloud dependenciesManage local model registries, versioning, and artifactsEnsure 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 workflowsWork with relational databases (Postgres/MySQL) and vector databases for RAG storage layersUse Docker and Git for reliable development and deployment pipelinesUse Azure DevOps for CI/CD, including local runners when applicable

Requirements

Technical Skills

Strong experience in Python for backend and machine learning developmentExpertise with ML frameworks such as PyTorch or TensorFlow, along with scikit-learn and pandasSolid knowledge of Postgres or MySQL for data storageExperience with Docker and GitHands-on experience with LLM training, fine-tuning, and optimizationExperience with Hugging Face Transformers & DatasetsFamiliarity with XML/XSD and Office document parsing toolsExperience deploying models with vLLM, TGI, or OllamaUnderstanding of quantization techniques such as GGUF, GPTQ, or AWQExperience with GPU optimization and the CUDA stackExperience building solutions for offline, on-prem, and air-gapped environmentsHands-on experience designing and implementing RAG pipelines, including embedding models, vector stores, and retrieval optimization strategiesExperience building or integrating MCP (Model Context Protocol) servers to connect LLMs with external tools, APIs, and structured data sourcesExperience with advanced RAG techniques such as HyDE or multi-hop retrievalExperience building agentic systems using MCP in production or near-production environments

Nice to Have

Experience managing ML model registries in offline environmentsFamiliarity with AWS for hybrid deploymentsExperience with secure environments, restricted networks, or enterprise compliance requirements

Soft Skills

Experience discussing complex technical topics with both technical and non-technical stakeholders