LLM Deployment Engineer

Destinus

. Deploy and maintain open-weights LLMs on cloud and local cluster .

Posted 11/5/2026full-timeZürich • SwitzerlandMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and maintaining open-weight LLMs, with a strong focus on cloud platforms and high-performance computing environments. Proficient in integrating security and privacy measures within automation pipelines while analyzing model effectiveness for enterprise applications.

Highest-signal resume keywords

LLM API ExperienceCI/CD Pipeline ProficiencyCloud Platform ExpertiseSecurity and Privacy UnderstandingModel Evaluation Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringLLM ApplicationsInfrastructure as CodeModel Context ProtocolAI GovernanceObservabilityOperational MonitoringHigh-Performance ComputingAutomation AnalysisRetrieval-Augmented Generation

Soft Skills

Problem-SolvingCollaboration

Tools & Technologies

AWSGCPAzureKubernetesSLURM

Industry Keywords

Open-Weights LLMsEnterprise EnvironmentsAutomation PipelineModel EffectivenessSensitive Content Safeguarding

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformKubernetes

About the role

Key responsibilities & impact

Deploy and maintain open-weights LLMs on cloud and local cluster
Scale deployments to serve a large number of users within the organization
Stay up-to-date with new model releases to keep deployments state of the art
Connect LLMs to specific use cases, helping users across the organization automate routine work
Analyze model effectiveness on automation tasks
Embed security and privacy in the automation pipeline to safeguard sensitive content

Requirements

What you’ll need

3+ years of professional experience in software engineering
Experience working with LLM APIs, both open-weight and proprietary
Familiarity with LLM applications, including tools, retrieval-augmented generation (RAG) and routing
Experience with CI/CD pipelines, infrastructure as code, and modern software delivery practices
Understanding of security and privacy considerations in enterprise environments
Strong problem-solving skills and the ability to collaborate effectively across technical and non-technical teams
Knowledge of Model Context Protocol (MCP) or similar interoperability standards
Experience with AI governance, observability, or operational monitoring
Proficiency with cloud platforms (AWS, GCP, or Azure), and high-performance computing environments (Kubernetes, SLURM)
Experience in evaluating ML models