AI Developer

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…