Salvo Software
Software
AIDeveloper
Neural analysis suggests this role is
optimal for Mid candidates.
“AI Developer at Salvo Software. Skills: LLM models, RAG pipelines, Python ML stacks, Offline deployment. Train and fine-tune LLMs. Work with open-source models”
Industry & Context.
problem-solving ability
on-prem environments, offline environments, air-gapped environments, restricted network environments
What They're Looking For.
Must Have
Python for backend and ML development, ML frameworks such as PyTorch or TensorFlow, scikit-learn, pandas, Postgres or MySQL for data storage, Docker, Git, DevOps best practices, LLM training, fine-tuning, and optimization, Hugging Face Transformers & Datasets, XML/XSD and Office document parsing tools, vLLM, TGI, or Ollama, quantization techniques (GGUF/GPTQ/AWQ), GPU optimization and the CUDA stack, solutions for offline, on-prem, and air-gapped environments, RAG pipelines, embedding models, vector stores (FAISS, Chroma, Weaviate, or pgvector), retrieval optimization strategies, MCP (Model Context Protocol) servers
Nice to Have
building agentic systems using MCP in production or near-production environments, advanced RAG techniques such as HyDE, re-ranking, or multi-hop retrieval, managing ML model registries in offline environments, AWS for hybrid deployments, secure environments, restricted networks, or enterprise compliance requirements
What You'll Do.
Train and fine-tune LLMs
Work with open-source models
Build LoRA / Q-LoRA pipelines
Implement data preprocessing workflows
Parse and process structured data
Implement document parsing solutions
Build and maintain vector stores
Optimize retrieval strategies
Develop MCP server integrations
Design agentic workflows
Deploy and maintain models offline
Perform model optimization and quantization
Build inference systems
Maintain local CI/CD pipelines
Manage local model registries
Ensure RAG and MCP components operational
Build backend services in Python
Work with relational databases
Use Azure DevOps for CI/CD
How You'll Work.
Team & Collaboration
work closely with our engineering and product teams; work effectively in distributed teams across time zones; discussing complex technical topics with both technical and non-technical stakeholders
Communication Scope
Clear communication when discussing complex technical topics with both technical and non-technical stakeholders
Full Job Description
**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 R
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