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.