AI Developer

Salvo Software

3d•Remote

About The Position

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.

Requirements

Strong experience in Python for backend and machine learning development.
Expertise with ML frameworks such as PyTorch or TensorFlow, along with scikit-learn and pandas.
Solid knowledge of Postgres or MySQL for data storage.
Experience with Docker and Git.
Hands-on experience 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 such as GGUF, GPTQ, or AWQ.
Experience with GPU optimization and the CUDA stack.
Experience building solutions for offline, on-prem, and air-gapped environments.
Hands-on experience designing and implementing RAG pipelines, including embedding models, vector stores, and retrieval optimization strategies.
Experience building or integrating MCP (Model Context Protocol) servers to connect LLMs with external tools, APIs, and structured data sources.
Experience with advanced RAG techniques such as HyDE or multi-hop retrieval.
Experience discussing complex technical topics with both technical and non-technical stakeholders.

Nice To Haves

Experience building agentic systems using MCP in production or near-production environments.
Experience managing ML model registries in offline environments.
Familiarity with AWS for hybrid deployments.
Experience with secure environments, restricted networks, or enterprise compliance requirements.

Responsibilities

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).
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.
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.
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.