MLOps Engineer

Valarian

. Lead deployment, monitoring, orchestration, and operational reliability of production LLM applications and an agentic AI platform .

Posted 10/22/2026full-timeLondon • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and managing LLM applications and AI platforms, with a strong focus on MLOps and Kubernetes. Proficient in automating workflows, monitoring AI performance, and optimizing cloud infrastructure for machine learning workloads.

Highest-signal resume keywords

MLOps ExperienceKubernetes ManagementPython ProficiencyMonitoring Tools ExpertiseCI/CD Pipeline Familiarity

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Applicant Tracking System Keywords

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Hard Skills

MLOpsLLMOpsPlatform EngineeringAI InfrastructureKubernetesPythonCI/CDInfrastructure as CodeMonitoringAgentic Architectures

Tools & Technologies

PrometheusGrafanaDatadogOpenTelemetryGitHub ActionsGitLab CIArgoCDTerraformPulumiLangSmith

Industry Keywords

AI WorkloadsContainerized ApplicationsCloud InfrastructureReal-Time Data PipelinesVector DatabasesRetrieval-Augmented GenerationAI Security PracticesCost Guardrails

Tech Stack

Tools & technologies

CloudGrafanaKubernetesPrometheusPythonTerraform

About the role

Key responsibilities & impact

Lead deployment, monitoring, orchestration, and operational reliability of production LLM applications and an agentic AI platform
Design, deploy, and manage LLM services, agentic applications, and supporting AI workloads in high-throughput Kubernetes clusters
Automate containerized deployments, scalable inference endpoints, and blue/green or canary rollout strategies through CI/CD pipelines
Support and maintain production agentic AI workflows, including multi-step tool execution, state management, and multi-agent coordination
Triage and resolve issues across AI application logic, orchestration frameworks, external API integrations, and cloud infrastructure
Build monitoring and evaluation frameworks for AI output accuracy, drift, latency, cost, and hallucination metrics
Implement trace-level observability for agent execution paths, including prompts, completions, tool calls, and agent steps
Monitor and optimize real-time and batch data pipelines, feature stores, vector indexing, and embedding updates
Manage GPU/CPU resources, memory, autoscaling, and cloud infrastructure expenditure to optimize time-to-first-token and throughput

Requirements

What you’ll need

3+ years of experience in MLOps, LLMOps, Platform Engineering, AI Infrastructure, or SRE with a focus on machine learning workloads
Hands-on experience managing and deploying complex containerized applications on Kubernetes (EKS, GKE, AKS, or bare metal) at scale
Strong proficiency in Python for backend automation, system debugging, custom tooling, and integration development
Proven experience with standard monitoring/logging tools (e.g., Prometheus, Grafana, Datadog, OpenTelemetry) and LLM-specific tracing and evaluation platforms (e.g., LangSmith, Phoenix/Arize, Langfuse, TruLens)
Familiarity with modern CI/CD pipelines (GitHub Actions, GitLab CI, ArgoCD) and Infrastructure as Code (IaC) tools (Terraform, Pulumi)
Deep operational understanding of agentic architectures, prompt chaining, function calling/tool execution, and non-deterministic application behavior
Practical experience operating Retrieval-Augmented Generation (RAG) pipelines and production vector databases (nice to have)
Hands-on exposure to agent development and orchestration frameworks (nice to have)
Experience with high-performance LLM serving engines and frameworks (nice to have)
Understanding of AI security practices and rate limiting/cost guardrails (nice to have)

Benefits

Comp & perks

Equity
A competitive salary
Employer pension contributions
Private health insurance
Hybrid work setup
Gym memberships
Rewarding company retreats and meetups that respect your work/life balance