MLOps Engineer

📋 Job Overview

Valarian is a dual use technology company building critical tools to safeguard the future in an era of evolving global security challenges. Backed by a Series A funding round of $50m led by NEA, we are rethinking security beyond traditional military domains to address asymmetric threats that impact our technological advantage, economic strength, and democratic institutions. We provide the control infrastructure that allows government and regulated enterprise to put frontier-class AI to work safely.

🏢 About Valarian

Valarian is bringing a frontier AI experience inside secure boundaries for the first time. Our technology supports mission critical workflows across government, healthcare, and enterprise, including scaling open-weight models, putting frontier class AI to work across organisations, increasing operational resiliency, securing intellectual property, and facilitating secure information flow during disaster response, M&A due diligence, and privileged legal communications. Built in Britain by veterans of the Ministry of Defence, British Forces, Darktrace, and elite engineering backgrounds, we are driven by a shared mission to safeguard western democracy.

🎯 The Role

We are looking for an experienced Senior MLOps / LLMOps Engineer to lead the deployment, monitoring, orchestration, and operational reliability of our production LLM applications and agentic AI platform. In this role, you will bridge the gap between AI development and production infrastructure, ensuring multi-step agentic workflows, data pipelines, and retrieval-augmented systems run reliably, securely, and at scale within a large-scale Kubernetes environment.

✅ Key Responsibilities

• Design, deploy, and manage LLM services, agentic applications, and supporting AI workloads inside high-throughput Kubernetes clusters.
• Automate containerized deployments, scalable inference endpoints, and blue/green or canary rollout strategies for LLM/Agent releases via modern CI/CD pipelines.
• Support and maintain the production operation of agentic AI workflows, including multi-step tool execution, state management, and multi-agent coordination.
• Rapidly triage and resolve issues across AI application logic, orchestration frameworks, external API integrations, and underlying cloud infrastructure.
• Build end-to-end monitoring and evaluation (Eval) frameworks to measure and maintain AI output accuracy, drift, latency, cost, and hallucination metrics.
• Implement trace-level observability for agent execution paths (tracking prompts, completions, tool calls, and agent steps).
• Monitor and optimize real-time and batch data pipelines powering AI workloads, including feature stores, vector indexing, and embedding updates.
• Manage resource allocation (GPU/CPU utilization), memory footprint, autoscaling strategies, and cloud infrastructure expenditure to optimize operational efficiency.