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Open nowPosted 6 hours ago

AI Developer

Workable (global search)107,765 open roles

Where
Mexico
Work mode
Remote
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Your applicationOpen nowAI DeveloperWorkable (global search) · Mexico

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Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 2 days.

Share of postings closed within
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  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.2%30 days
This job: posted 6 hours ago

Workable (global search) median: 2 days open

The posting

About Salvo Software

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 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.

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

Required

  • 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.

Should Have (can ramp up with mentorship)

  • 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).

Nice to Have

  • 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.

Soft Skills

  • 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.
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