AI Developer at Salvo Software - Remote LATAM/India — Columbus
Traveler-Friendly Insight
Remote-First, Distributed Team: Salvo Software explicitly operates with distributed teams across the US, LATAM, and India — a strong signal that async and cross-timezone collaboration is baked into their culture. This is a genuinely location-independent role, not an office job with a remote label.
Timezone Flexibility: The job description does not specify fixed hours or a required timezone overlap window, which is relatively nomad-friendly. However, with teammates in the US, LATAM, and India, expect some overlap windows to be needed — likely US Eastern or Central hours for core sync meetings.
Visa Considerations: Since the role is open to LATAM and India, digital nomad visas in popular destinations are viable options:
- Portugal (D8 Digital Nomad Visa) — solid option for LATAM-based candidates or those wanting EU access.
- Colombia (Digital Nomad Visa) — 2-year visa, low cost of living in Medellín (~$1,200–1,800/mo).
- Thailand (LTR Visa or tourist visa runs) — popular for tech workers; Bangkok costs ~$1,000–1,500/mo.
No Salary Listed: Without published compensation, budget planning is difficult. At market rates for a senior LLM engineer (US: $130k–$180k+), Medellín or Bangkok offer exceptional purchasing power. Even a mid-range LATAM salary would stretch significantly in Southeast Asia.
Travel-Relevant Perks: No specific perks like coworking allowances or equipment stipends are mentioned. Clarify during interviews whether hardware (GPU workstation for local LLM inference) is provided, as this role involves running open-source models 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; 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 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); 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.
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