Senior AI/LLM Platform Engineer

Avenga

. Design and build on-premise and cloud environments for hosting LLMs .

Posted 11/4/2026full-timeSofia • BulgariaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in platform engineering and ML infrastructure, with a strong focus on deploying and optimizing open-weight models in both on-premise and cloud environments. Proficient in managing GPU infrastructure and utilizing tools like Docker and Kubernetes for efficient model serving and performance monitoring.

Highest-signal resume keywords

Platform EngineeringOpen-Weight Model DeploymentGPU Infrastructure ManagementDocker and Kubernetes SkillsInference Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Platform EngineeringOpen-Weight Model DeploymentInference OptimizationGPU InfrastructureNVIDIA DriversCUDAKubernetesTerraformCloud GPU ServicesAI Coding Tools

Soft Skills

Fluent English

Tools & Technologies

DockerKubernetesTerraformOpenTofuAWSGCPAzureVLLMSGLangOllama

Industry Keywords

ML InfrastructureInference EnginesModel OptimizationGPU SchedulingCapacity Planning

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformKubernetesTerraform

About the role

Key responsibilities & impact

Design and build on-premise and cloud environments for hosting LLMs
Deploy, serve, and scale open-weight models behind standard APIs such as OpenAI-compatible endpoints
Optimize models and serving configuration for throughput, latency, and GPU cost
Benchmark optimization results
Administer the platform, including upgrades, capacity planning, access control, quotas, and the model catalog
Monitor performance, GPU utilization, usage, and cost
Evaluate new models, inference engines, and hardware and recommend what to adopt
Secure the environment, including isolating client data and controlling access
Support engineering teams and AI agents that use the platform

Requirements

What you’ll need

5+ years of experience in platform engineering, DevOps, infrastructure, or ML infrastructure
Hands-on experience deploying and serving open-weight models such as Llama, Qwen, Mistral, or Gemma
Experience with inference engines such as vLLM, SGLang, Ollama, or llama.cpp
Practical knowledge of inference optimization, including quantization, batching, and multi-GPU deployment
Experience matching models to available hardware
Experience with GPU infrastructure, including NVIDIA drivers and CUDA
Experience with GPU scheduling on Kubernetes
Experience monitoring GPU utilization
Experience with on-premise infrastructure and at least one cloud provider: AWS, GCP, or Azure
Experience with cloud GPU and AI services
Strong Docker and Kubernetes skills
Experience with infrastructure as code using Terraform or OpenTofu
Hands-on experience with at least one modern AI coding tool, such as Claude Code, Codex, Cursor, or GitHub Copilot
Fluent English

Benefits

Comp & perks

Equal opportunities regardless of race, ethnicity, gender identity, sexual orientation, disability, age, religion, or any other characteristic
Respectful and inclusive work environment
Opportunity to explore new models, inference engines and hardware as the field evolves
Opportunity to support AI-native engineering and business use cases across client projects