AI Developer at Salvo Software - Remote LATAM & US — Columbus
Traveler-Friendly Insight
Remote-friendliness: Salvo Software operates with distributed teams across the US, LATAM, and India — a strong signal that async-friendly workflows are baked into their culture. This role appears genuinely location-independent within the allowed regions, making it well-suited for nomads based in the Americas or South/Southeast Asia.
Timezone considerations: With teams in LATAM and the US, you'll likely need overlap with US Eastern or Central time (roughly UTC-6 to UTC-5). Popular nomad bases that fit well include:
- Medellín, Colombia (UTC-5, ~$1,200–1,800/mo cost of living) — ideal timezone alignment, strong tech community, and affordable coworking.
- Mexico City, Mexico (UTC-6, ~$1,500–2,200/mo) — explicitly mentioned in the location hint, excellent infrastructure, and direct overlap with US clients.
- Lisbon, Portugal (UTC+0/+1) — may require early-morning overlap with US teams; feasible but less ideal.
Visa options: Mexico offers a Temporary Resident visa and a Remote Worker visa pathway. Colombia has a digital nomad visa (up to 2 years). Brazil and Argentina also have nomad visa programs. US citizens can work from most LATAM countries on tourist visas for shorter stints (90 days).
Perks for nomads: No specific equipment stipend or coworking allowance is mentioned, so budget for your own setup — especially GPU access if working on model quantization tasks locally.
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 and 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).
Requirements
- 3–5+ years of 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 are a plus).
- Cloud & Infrastructure expertise covering Azure/AWS deployments, Docker containerization, Linux/Windows admin, and CI/CD automation (Azure DevOps, GitHub Actions, or GitLab CI); Terraform, Kubernetes, Kafka, or systems languages (Go, Rust, C++) are 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.