Software Engineer (LLMOps)

About The Company The Best of Both Worlds At Maisa, we believe great work happens when people have the freedom to choose the setup that works best for them. Prefer the flexibility of remote work? You’re welcome to work remotely. Most of our roles are currently focused on Spain, although depending on the position and profile, we may be open to hiring from other locations. Looking for some social energy? Our hybrid setup gives you the option to connect and work alongside teammates at our hubs in Valencia, Barcelona, Madrid and Murcia. And as we grow, our spaces will grow with us: when we build a team of more than 10 people in a location, our ambition is to create a hub there too. We also want the time we spend together in person to be worth it. Today, that includes weekly transportation from the city center to our Valencia headquarters and weekly team lunches in Valencia and Barcelona, with more experiences to come as our teams and hubs evolve. The Culture is OURS to Create We’re still shaping how we work together—and that’s part of what makes joining Maisa now specifically exciting. Everyone who joins has the opportunity to influence how we collaborate, connect, and build our culture. Rather than inheriting a finished playbook, you’ll have a voice in creating it. Remote when you want flexibility. Together when you want connection. And a culture you can help shape from day one. 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. Join us in building the infrastructure that makes enterprise AI reliable, scalable, and accessible. By submitting this application, I agree that my personal data will be collected, processed, and retained by the company solely for the purposes of managing and assessing my candidacy.