Salvo Software is a global technology company specializing in custom software development and advanced engineering solutions. 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. We’re growing our AI department and are looking for a hands-on AI Developer to help build it.
Role Overview
We are looking for an AI Developer to join and strengthen our AI department. Your core work will be building and operating LLM-powered systems: serving open-source models with Ollama and llama.cpp, building RAG pipelines, and developing MCP integrations that connect large language models to real tools and data. Solid DevOps fundamentals - Docker, CI/CD, Azure - support this work, but AI engineering is the heart of the role.
You don’t need to be a deep ML researcher. What matters is production-grade Python, strong fundamentals, and the aptitude to learn fast. You’ll work closely with our engineering and product teams to take LLM-powered features from prototype to reliably deployed systems, with mentorship available as you ramp up in areas like RAG architecture, Kafka, and advanced MCP work.
Key Responsibilities
AI / LLM Engineering (core focus)

• Serve and operate open-source LLMs using Ollama and llama.cpp, locally and in on-prem environments.

• Build and maintain RAG pipelines: embeddings, vector databases, chunking strategies, and retrieval quality.

• Develop MCP (Model Context Protocol) integrations connecting LLMs to internal tools and data sources.

• Build backend services in Python that power ML inference and AI-driven product features.

• Parse and process structured and semi-structured data (XML/XSD, Office document formats) as pipeline inputs.

• Grow into model optimization over time: quantization (GGUF), GPU/CUDA tuning, and offline/air-gapped deployments.
  DevOps & Infrastructure (supporting)

• Build and maintain CI/CD pipelines for AI services (Azure DevOps preferred; GitHub Actions / GitLab CI also used).

• Deploy AI workloads to Microsoft Azure,AWS and containerize services with Docker.

• Automate operational tasks with Python, Bash and/or PowerShell scripting.

• Troubleshoot across the stack - dig into root causes rather than patching symptoms.

• Support event-driven architectures using Apache Kafka (producers/consumers).

• 3–5+ years engineering experience with production-level Python for building real services, LLM inference, RAG pipelines, and automated tooling.

• Hands-on LLM & AI integration serving models via Ollama/llama.cpp, implementing vector retrieval/RAG, and developing MCP integrations (GPU/CUDA tuning & model quantization nice to have).

• Cloud & Infrastructure expertise covering Azure/AWS deployments, Docker containerization, Linux/Windows admin, and CI/CD automation (Azure DevOps, GitHub Actions, or GitLab CI), with Terraform, Kubernetes, Kafka, or systems languages (Go, Rust, C++) as a plus.

• DevOps, Security & Operations awareness, including Git workflows, scripting (Python, Bash, PowerShell), observability tools (Prometheus/Grafana), DevSecOps, and compliance frameworks (SOC 2, ISO 27001, Zero Trust).

• Strong problem-solving & communication with a root-cause troubleshooting mindset, self-directed learning, strong ownership, and seamless remote/distributed team collaboration.

Salvo Software is a global technology company specializing in custom software development and advanced engineering solutions. 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. We’re growing our AI department and are looking for a hands-on AI Developer to help build it.
Role Overview
We are looking for an AI Developer to join and strengthen our AI department. Your core work will be building and operating LLM-powered systems: serving open-source models with Ollama and llama.cpp, building RAG pipelines, and developing MCP integrations that connect large language models to real tools and data. Solid DevOps fundamentals - Docker, CI/CD, Azure - support this work, but AI engineering is the heart of the role.
You don’t need to be a deep ML researcher. What matters is production-grade Python, strong fundamentals, and the aptitude to learn fast. You’ll work closely with our engineering and product teams to take LLM-powered features from prototype to reliably deployed systems, with mentorship available as you ramp up in areas like RAG architecture, Kafka, and advanced MCP work.
Key Responsibilities
AI / LLM Engineering (core focus)

• Serve and operate open-source LLMs using Ollama and llama.cpp, locally and in on-prem environments.

• Build and maintain RAG pipelines: embeddings, vector databases, chunking strategies, and retrieval quality.

• Develop MCP (Model Context Protocol) integrations connecting LLMs to internal tools and data sources.

• Build backend services in Python that power ML inference and AI-driven product features.

• Parse and process structured and semi-structured data (XML/XSD, Office document formats) as pipeline inputs.

• Grow into model optimization over time: quantization (GGUF), GPU/CUDA tuning, and offline/air-gapped deployments.
  DevOps & Infrastructure (supporting)

• Build and maintain CI/CD pipelines for AI services (Azure DevOps preferred; GitHub Actions / GitLab CI also used).

• Deploy AI workloads to Microsoft Azure,AWS and containerize services with Docker.

• Automate operational tasks with Python, Bash and/or PowerShell scripting.

• Troubleshoot across the stack - dig into root causes rather than patching symptoms.

• Support event-driven architectures using Apache Kafka (producers/consumers).

• 3–5+ years engineering experience with production-level Python for building real services, LLM inference, RAG pipelines, and automated tooling.

• Hands-on LLM & AI integration serving models via Ollama/llama.cpp, implementing vector retrieval/RAG, and developing MCP integrations (GPU/CUDA tuning & model quantization nice to have).

• Cloud & Infrastructure expertise covering Azure/AWS deployments, Docker containerization, Linux/Windows admin, and CI/CD automation (Azure DevOps, GitHub Actions, or GitLab CI), with Terraform, Kubernetes, Kafka, or systems languages (Go, Rust, C++) as a plus.

• DevOps, Security & Operations awareness, including Git workflows, scripting (Python, Bash, PowerShell), observability tools (Prometheus/Grafana), DevSecOps, and compliance frameworks (SOC 2, ISO 27001, Zero Trust).

• Strong problem-solving & communication with a root-cause troubleshooting mindset, self-directed learning, strong ownership, and seamless remote/distributed team collaboration.