Software Engineer (LLMOps)
First seen by Alion on Oct 29, 2026.
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.
Tech stack
- vLLM
- SGLang
- GPU infrastructure
- Kubernetes
- Docker
- Terraform
- CI/CD