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MLOps Engineer

Valarian
London, GBR
Full-time
Mid · 3+ years experience
Hybrid
Discovered 3 days ago
langchain

Full Job Posting

What you’ll do

  • D esign, 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.

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