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 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. Originally posted on Himalayas