Auf einen Blick
Du baust und betreibst LLM-gestützte Produktsysteme mit RAG-Pipelines und MCP-Integrationen in Python. 3-5+ Jahre Backend-Erfahrung und KI-Integration erforderlich. Remote-Position in den USA.
💰 ~$120.000–170.000/Jahr (geschätzt)
📊 Mid-Level
🕒 Vollzeit
🌍 Remote
🗺️ Americas
- 3-5+ Jahre Python/Backend
- LLM und RAG Erfahrung
- Azure/AWS Expertise
- CI/CD Erfahrung
Python
RAG-Pipelines
Ollama/llama.cpp
Docker
CI/CD
Azure
AWS
Kafka
Gehalt geschätzt anhand Senior Backend KI-Ingenieur in USA, Remote. Kein Gehalt in der Anzeige angegeben.
✅ Geeignet für
- Backend-Ingenieure mit KI-Interesse
- Personen mit Python und Cloud-Expertise
- Kandidaten, die in schnell wachsendem KI-Sektor arbeiten möchten
🚫 Weniger geeignet
- Junior-Entwickler ohne 3+ Jahre Backend-Erfahrung
- Personen ohne praktische LLM- oder RAG-Erfahrung
- Kandidaten mit nur oberflächlichen DevOps-Kenntnissen
💡 Gut zu wissen
- RAG und LLM-Optimierung sind Kernkompetenz, nicht optional
- Produktionsbereitschaft und Observability sind kritisch
- Remote-Umgebung erfordert starke Eigenverantwortung
- GPU/CUDA-Tuning und Modelloptimierung sind längerfristige Entwicklungswege
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.
