AI Developer

🕒 May 4

🇲🇽 Mexico – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

👻 Ghost score 40%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Salvo Software

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤝 B2B

☁️ SaaS

🏢 Enterprise

B2B • SaaS • Enterprise

Salvo Software is a global software development company headquartered in Vancouver, WA, with near-shoring capabilities that offer a blend of international pricing and U. S. standards. The company specializes in custom software solutions, providing services such as software product development, AI-enabled applications, blockchain development, IoT, speech recognition, web application development, and business automation. Salvo Software is dedicated to helping enterprises and startups develop strategic products through agile development processes and adaptable, affordable services. Their expertise includes UI/UX design, QA and testing, embedded firmware development, and cloud application development, supporting various industries with tailored solutions like e-commerce platforms, CRM systems, and educational resource platforms.

📋 Description

• Serve and operate open-source LLMs using Ollama and llama.cpp locally and in on-prem environments. • Build and maintain RAG pipelines involving embeddings, vector databases, chunking strategies, and retrieval quality. • Develop MCP integrations connecting LLMs to internal tools and data sources. • Build Python backend services powering ML inference and AI-driven product features. • Parse and process structured and semi-structured data, including XML/XSD and Office document formats, as pipeline inputs. • Grow into model optimization including GGUF quantization, GPU/CUDA tuning, and offline/air-gapped deployments. • Build and maintain CI/CD pipelines for AI services. • Deploy AI workloads to Microsoft Azure and AWS and containerize services with Docker. • Automate operational tasks with Python, Bash, and/or PowerShell. • Troubleshoot across the stack and investigate root causes. • Support event-driven architectures using Apache Kafka producers and consumers. • Collaborate with engineering and product teams to take LLM-powered features from prototype to reliably deployed systems.

🎯 Requirements

• Production-level Python — real services and pipelines, not just scripts. • Hands-on experience serving LLMs with Ollama and/or llama.cpp. • 3–5 years of hands-on experience across backend, ML engineering, DevOps, or infrastructure. • Working knowledge of Docker and Linux fundamentals. • Experience with Microsoft Azure, AWS, and cloud-based deployments. • Practical experience building and maintaining CI/CD pipelines; Azure DevOps strongly preferred, with GitHub Actions / GitLab CI also relevant. • Comfortable scripting in Python, Bash and/or PowerShell. • Strong Git fundamentals and branching/workflow discipline. • Troubleshooting mindset; able to work through ambiguity and dig into root causes. • Fast learner with genuine aptitude and willingness to pick up new tools quickly. • Good communication and collaboration skills; comfortable in a remote, distributed team. • GPU/CUDA troubleshooting experience. • RAG concepts: vector databases, embeddings, and chunking strategies. • Advanced MCP (Model Context Protocol) knowledge. • Kubernetes (kubectl basics). • Infrastructure as code with Terraform. • Apache Kafka fundamentals, including producer/consumer patterns. • A systems language: Go, Rust, C++, or Zig. • Deeper llama.cpp knowledge, including building and quantizing models. • Observability tooling such as Prometheus/Grafana and SRE practices. • Exposure to security and compliance frameworks such as SOC 2, ISO 27001, and Zero Trust. • Familiarity with DevSecOps practices and secure pipeline design. • Experience deploying Kotlin or other JVM-based applications. • Linux and Windows systems administration background. • Ability to explain why something broke, not just that it did. • Comfortable saying “I don't know — I'll find out.” • Self-directed learner through side projects, home lab, or open-source contributions. • Takes feedback well and asks good questions. • Strong ownership and problem-solving ability across time zones.