Software Engineer (LLMOps)
⚲ Manises
Do uzgodnienia
Wymagania
- vLLM
- SGLang
- GPU infrastructure
- Kubernetes
- Docker
- Terraform
- CI/CD
Opis stanowiska
What you will do
• Build and optimise LLM serving infrastructure using technologies such as vLLM, SGLang, or equivalent inference engines.
• Manage GPU infrastructure, including orchestration, scaling, load balancing, capacity planning, and high availability.
• Optimise inference performance and costs through batching, quantisation, caching, and GPU utilisation improvements.
• Build monitoring and observability for model serving, covering infrastructure health, latency, errors, and model output degradation.
• Support enterprise deployments into on-premise, private, and air-gapped environments, ensuring security and reliability.
• Develop model governance infrastructure, including model registries, versioning, LLM gateways, logging, and auditing.
• Build and maintain CI/CD pipelines and infrastructure-as-code for automated, reliable model deployments.
• Troubleshoot production issues and collaborate with Applied AI, Foundational AI, and SRE/Platform teams.
What We're Looking For
• Strong background in Infrastructure Engineering, Platform Engineering, MLOps, or LLMOps, with experience running ML/LLM workloads in production.
• Hands-on experience with LLM serving technologies such as vLLM, SGLang, or similar.
• Practical knowledge of GPU infrastructure, orchestration, scaling, and inference optimisation.
• Strong production experience with Kubernetes, Docker, Terraform, and CI/CD.
• Proficiency in Python and/or Go.
• Experience with monitoring, observability, and troubleshooting production systems.
• Understanding of inference cost, latency, throughput, and performance trade-offs.
• Strong ownership mindset and ability to collaborate across engineering teams.
Nice to Have
• Experience with on-premise, air-gapped, or regulated enterprise environments.
• Familiarity with LiteLLM, Langfuse, Prometheus, Grafana, or similar tools.
• Experience with model registries, prompt versioning, and LLM governance.
• Knowledge of distributed inference and advanced GPU optimisation.
Why Join Maisa?
• Work on cutting-edge enterprise AI with real-world applications and major enterprise customers.
• Solve challenging engineering problems involving GPU infrastructure, inference performance, and secure deployments.
• Take ownership and make an impact on the architecture, reliability, and cost-efficiency of our AI platform.
• Collaborate with talented AI and engineering teams in a fast-growing, ambitious company.
•
Remote when you want flexibility. Together when you want connection. And a culture you can help shape from day one.
Join us in building the infrastructure that makes enterprise AI reliable, scalable, and accessible.
• Build and optimise LLM serving infrastructure using technologies such as vLLM, SGLang, or equivalent inference engines.
• Manage GPU infrastructure, including orchestration, scaling, load balancing, capacity planning, and high availability.
• Optimise inference performance and costs through batching, quantisation, caching, and GPU utilisation improvements.
• Build monitoring and observability for model serving, covering infrastructure health, latency, errors, and model output degradation.
• Support enterprise deployments into on-premise, private, and air-gapped environments, ensuring security and reliability.
• Develop model governance infrastructure, including model registries, versioning, LLM gateways, logging, and auditing.
• Build and maintain CI/CD pipelines and infrastructure-as-code for automated, reliable model deployments.
• Troubleshoot production issues and collaborate with Applied AI, Foundational AI, and SRE/Platform teams.
What We're Looking For
• Strong background in Infrastructure Engineering, Platform Engineering, MLOps, or LLMOps, with experience running ML/LLM workloads in production.
• Hands-on experience with LLM serving technologies such as vLLM, SGLang, or similar.
• Practical knowledge of GPU infrastructure, orchestration, scaling, and inference optimisation.
• Strong production experience with Kubernetes, Docker, Terraform, and CI/CD.
• Proficiency in Python and/or Go.
• Experience with monitoring, observability, and troubleshooting production systems.
• Understanding of inference cost, latency, throughput, and performance trade-offs.
• Strong ownership mindset and ability to collaborate across engineering teams.
Nice to Have
• Experience with on-premise, air-gapped, or regulated enterprise environments.
• Familiarity with LiteLLM, Langfuse, Prometheus, Grafana, or similar tools.
• Experience with model registries, prompt versioning, and LLM governance.
• Knowledge of distributed inference and advanced GPU optimisation.
Why Join Maisa?
• Work on cutting-edge enterprise AI with real-world applications and major enterprise customers.
• Solve challenging engineering problems involving GPU infrastructure, inference performance, and secure deployments.
• Take ownership and make an impact on the architecture, reliability, and cost-efficiency of our AI platform.
• Collaborate with talented AI and engineering teams in a fast-growing, ambitious company.
•
Remote when you want flexibility. Together when you want connection. And a culture you can help shape from day one.
Join us in building the infrastructure that makes enterprise AI reliable, scalable, and accessible.
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