AI Developer

Salvo Software · posted Nov 5, 2026

Open to candidates in Mexico and United States

Full-time · Mid-level

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What this job asks for

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Must have

  • •3-5+ years production Python experience
  • •Hands-on LLM/AI integration (Ollama/llama.cpp)
  • •RAG pipeline implementation
  • •Cloud & Infrastructure (Azure/AWS, Docker, CI/CD)
  • •Strong problem-solving & communication

Nice to have

  • •GPU/CUDA tuning
  • •Model quantization
  • •Offline/air-gapped deployments
  • •Terraform, Kubernetes, Kafka
  • •Systems languages (Go, Rust, C++)

Tools and technologies

Python, Ollama, llama.cpp, RAG, MCP, Docker, CI/CD, Azure, Azure DevOps, GitHub Actions, GitLab CI, AWS, Bash, PowerShell, Kafka

Read the full posting

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).

Requirements

  • 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.
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