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 pipelines for ML models without cloud dependencies.
-
Manage local model registries, versioning, and artifacts.
-
Ensure 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 workflows.
-
Work with relational databases (Postgres/MySQL) and vector databases for RAG storage layers.
-
Use Docker and Git for reliable development and deployment pipelines.
-
Use Azure DevOps for CI/CD, including local runners when applicable.
Requirements
Technical Skills
-
Strong experience in Python for backend and ML development.
-
Expertise with ML frameworks such as PyTorch or TensorFlow, scikit-learn, and pandas.
-
Solid knowledge of Postgres or MySQL for data storage.
-
Experience with Docker, Git, and DevOps best practices.
-
Hands-on expertise 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 quantization techniques (GGUF/GPTQ/AWQ).
-
Experience working with GPU optimization and the CUDA stack.
-
Ability to build solutions for offline, on-prem, and air-gapped environments.
-
Hands-on experience designing and implementing RAG pipelines, including embedding models, vector stores (FAISS, Chroma, Weaviate, or pgvector), and retrieval optimization strategies.
-
Experience building or integrating MCP (Model Context Protocol) servers to connect LLMs with external tools, APIs, and structured data sources.
Nice to Have
-
Experience building agentic systems using MCP in production or near-production environments.
-
Familiarity with advanced RAG techniques such as HyDE, re-ranking, or multi-hop retrieval.
-
Experience managing ML model registries in offline environments.
-
Familiarity with AWS for hybrid deployments.
-
Experience with secure environments, restricted networks, or enterprise compliance requirements.
Soft Skills
-
Strong ownership mindset and problem-solving ability.
-
Ability to work effectively in distributed teams across time zones.
-
Clear communication when discussing complex technical topics with both technical and non-technical stakeholders.
