AI Developer

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.

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 languange 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.

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).

• 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; GitHub Actions / GitLab CI also relevant).
• Comfortable scripting in Python, Bash and/or PowerShell.
• Strong Git fundamentals and branching/workflow discipline.
• A 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, chunking strategies.
• Advanced MCP (Model Context Protocol) knowledge.
• Kubernetes (kubectl basics).
• Infrastructure as code with Terraform.
• Apache Kafka fundamentals (producer/consumer patterns).

• A systems language: Go, Rust, C++, or Zig.
• llama.cpp at a deeper level - building and quantizing models.
• Observability tooling (Prometheus/Grafana) and SRE practices.
• Exposure to security and compliance frameworks (SOC 2, ISO 27001, Zero Trust).
• Familiarity with DevSecOps practices and secure pipeline design.
• Experience deploying Kotlin (or other JVM-based) applications.
• Linux and Windows systems administration background.

• Can explain why something broke, not just that it did.
• Comfortable saying "I don't know - I'll find out."
• Self-directed learner (side projects, home lab, open-source contributions).
• Takes feedback well and asks good questions.
• Strong ownership and problem-solving ability across time zones.