MLOps Engineer
London
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type Full time
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign, deploy and manage LLM services and agent apps on Kubernetes clusters
Automate containerized releases with blue/green or canary rollouts via CI/CD
Build monitoring and evaluation frameworks for accuracy, drift, latency and hallucinations
Triage and resolve issues across AI logic, orchestration, APIs and cloud infrastructure
Manage GPU/CPU resources, autoscaling and cloud spend to improve TTFT and throughput
From the employer’s posting
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.
Build end-to-end monitoring and evaluation (Eval) frameworks to measure and maintain AI output accuracy, drift, latency, cost, and hallucination metrics.
Rapidly triage and resolve issues across AI application logic, orchestration frameworks, external API integrations, and underlying cloud infrastructure.
Manage resource allocation (GPU/CPU utilization), memory footprint, autoscaling strategies, and cloud infrastructure expenditure to optimize time-to-first-token (TTFT) and throughput.
What you’ll bring
All qualificationsCore experience
- 3+ years of experience in MLOps, LLMOps, Platform Engineering, AI Infrastructure, or SRE with a focus on machine learning workloads.
- Practical experience operating Retrieval-Augmented Generation (RAG) pipelines and production vector databases (e.g., Pinecone, Qdrant, Milvus, pgvector, Weaviate).
- 3+ years of experience in MLOps, LLMOps, Platform Engineering, AI Infrastructure, or SRE with a focus on machine learning workloads.
- Practical experience operating Retrieval-Augmented Generation (RAG) pipelines and production vector databases (e.g., Pinecone, Qdrant, Milvus, pgvector, Weaviate).
- Hands-on experience managing and deploying complex containerized applications on Kubernetes (EKS, GKE, AKS, or bare metal) at scale.
- Experience with high-performance LLM serving engines and frameworks (e.g., vLLM, TGI, Ollama, TensorRT-LLM).
Qualification wording
3+ years of experience in MLOps, LLMOps, Platform Engineering, AI Infrastructure, or SRE with a focus on machine learning workloads.
Practical experience operating Retrieval-Augmented Generation (RAG) pipelines and production vector databases (e.g., Pinecone, Qdrant, Milvus, pgvector, Weaviate).
Hands-on experience managing and deploying complex containerized applications on Kubernetes (EKS, GKE, AKS, or bare metal) at scale.
Experience with high-performance LLM serving engines and frameworks (e.g., vLLM, TGI, Ollama, TensorRT-LLM).
Tools in this posting
- Python
- Datadog
- Grafana
- Kubernetes
- Terraform
Source — Tool mentions in context
- 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) as well as LLM-specific tracing and evaluation platforms (e.g., LangSmith, Phoenix/Arize, Langfuse, TruLens).
- 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) as well as 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).
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. What you’ll do:
What you’ll do: - 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.
- 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) as well as 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.
Benefits in the posting
Full benefits wording- A competitive salary – because we value your unique skills
- Employer pension contributions – because you deserve a secure future
- Private health insurance - because your health is important to us
- Hybrid work setup – because everyone has different needs
- Gym memberships - because staying active fuels productivity
- Life at Valarian
- We are bringing a frontier AI experience inside secure boundaries for the first time. Our technology supports mission critical workflows across government, healthcare, and enterprise, including:
- We’ve only scratched the surface. 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 . Whether you join our London HQ or our wider global organisation, you’ll be part of a collaborative, high performing team creating the cutting edge software and infrastructure that defines the frontier of enterprise AI.
From the employer’s posting.
Posting record
Report an errorActively hiringFirst seen by us 4 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 24, 2026 · 4 days ago
- Recorded sightings
- 5
- Last seen by us
- Oct 29, 2026
- Employer says posted
- Oct 22, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
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Job description
Valarian: A New Era of Sovereign Intelligence
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.
We are 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 without external inference endpoints.
-
Putting frontier class AI to work across organisations through partnerships with other leading AI startups such as Perplexity.
-
Increasing operational resiliency for air gapped, on prem, and cloud based systems.
-
Securing intellectual property and maintaining auditability for high stakes enterprise environments.
-
Facilitating secure information flow during disaster response, M&A due diligence, and privileged legal communications.
We’ve only scratched the surface. 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 . Whether you join our London HQ or our wider global organisation, you’ll be part of a collaborative, high performing team creating the cutting edge software and infrastructure that defines the frontier of enterprise AI.
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.
What you’ll do:
-
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 time-to-first-token (TTFT) and throughput.
What we are looking for:
-
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) as well as 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.
Nice to have:
-
Practical experience operating Retrieval-Augmented Generation (RAG) pipelines and production vector databases (e.g., Pinecone, Qdrant, Milvus, pgvector, Weaviate).
-
Hands-on exposure to agent development and orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI).
-
Experience with high-performance LLM serving engines and frameworks (e.g., vLLM, TGI, Ollama, TensorRT-LLM).
-
Understanding of AI security practices (prompt injection mitigation, data privacy, PII masking) and rate limiting/cost guardrails.
Benefits:
Our benefits are designed to ensure our employees feel taken care of and are proud to be a part of the Valarian team. We are committed to consistently enhancing our benefit package, taking into account the overall well-being and needs of our teammates. Here are the key benefits accessible to all employees at Valarian Technologies:
-
Equity – because you have the right to own what you’re building
-
A competitive salary – because we value your unique skills
-
Employer pension contributions – because you deserve a secure future
-
Private health insurance - because your health is important to us
-
Hybrid work setup – because everyone has different needs
-
Gym memberships - because staying active fuels productivity
-
Rewarding company retreats and meetups that respect your work/life balance – because we love getting to know each other
Life at Valarian
Our culture is built on inclusivity, compassion and flexibility – we want everyone to be empowered to achieve their goals at Valarian.
The work we do is vital, but so are the connections that make it happen. We thrive on the shared energy, spontaneous conversations, and mutual trust built when we spend time together. We operate on an office first hybrid model, regularly gathering in our London office to support one another and collaborate.
Valarian: A New Era of Sovereign Intelligence
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.
We are 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 without external inference endpoints.
-
Putting frontier class AI to work across organisations through partnerships with other leading AI startups such as Perplexity.
-
Increasing operational resiliency for air gapped, on prem, and cloud based systems.
-
Securing intellectual property and maintaining auditability for high stakes enterprise environments.
-
Facilitating secure information flow during disaster response, M&A due diligence, and privileged legal communications.
We’ve only scratched the surface. 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 . Whether you join our London HQ or our wider global organisation, you’ll be part of a collaborative, high performing team creating the cutting edge software and infrastructure that defines the frontier of enterprise AI.
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.
What you’ll do:
-
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 time-to-first-token (TTFT) and throughput.
What we are looking for:
-
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) as well as 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.
Nice to have:
-
Practical experience operating Retrieval-Augmented Generation (RAG) pipelines and production vector databases (e.g., Pinecone, Qdrant, Milvus, pgvector, Weaviate).
-
Hands-on exposure to agent development and orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI).
-
Experience with high-performance LLM serving engines and frameworks (e.g., vLLM, TGI, Ollama, TensorRT-LLM).
-
Understanding of AI security practices (prompt injection mitigation, data privacy, PII masking) and rate limiting/cost guardrails.
Benefits:
Our benefits are designed to ensure our employees feel taken care of and are proud to be a part of the Valarian team. We are committed to consistently enhancing our benefit package, taking into account the overall well-being and needs of our teammates. Here are the key benefits accessible to all employees at Valarian Technologies:
-
Equity – because you have the right to own what you’re building
-
A competitive salary – because we value your unique skills
-
Employer pension contributions – because you deserve a secure future
-
Private health insurance - because your health is important to us
-
Hybrid work setup – because everyone has different needs
-
Gym memberships - because staying active fuels productivity
-
Rewarding company retreats and meetups that respect your work/life balance – because we love getting to know each other
Life at Valarian
Our culture is built on inclusivity, compassion and flexibility – we want everyone to be empowered to achieve their goals at Valarian.
The work we do is vital, but so are the connections that make it happen. We thrive on the shared energy, spontaneous conversations, and mutual trust built when we spend time together. We operate on an office first hybrid model, regularly gathering in our London office to support one another and collaborate.
Employment type
Full time
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
London
- Private health insurance - because your health is important to us - Hybrid work setup – because everyone has different needs - Gym memberships - because staying active fuels productivity
More source context
Our culture is built on inclusivity, compassion and flexibility – we want everyone to be empowered to achieve their goals at Valarian. The work we do is vital, but so are the connections that make it happen. We thrive on the shared energy, spontaneous conversations, and mutual trust built when we spend time together. We operate on an office first hybrid model, regularly gathering in our London office to support one another and collaborate. Valarian: A New Era of Sovereign Intelligence
More relevant text appears in the full description.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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