ppWe are looking for a bMLOps / AIOps / LLMOps / AgentOps Engineer /b to join a multidisciplinary Data AI team. The main mission of this role is to bdesign, operate, and continuously evolve our AIOps platform /b , ensuring that our AI products run in a breliable, scalable, and cost‑efficient /b way. /ppThis position is bstrongly focused on platform, infrastructure, automation, observability, and operations /b rather than on building ML models or AI products themselves. /ppYou will work with modern cloud technologies (mainly bAWS /b , with some bAzure /b exposure) and collaborate closely with bData Scientists, Data Engineers, and Product teams /b to bring AI solutions into production and keep them running smoothly. /ppWe are open to candidates with bstrong expertise in at least one core area /b (e.g. cloud, DevOps, platform engineering, or ML operations) and bsolid foundational knowledge in the others /b , with motivation to grow across the full AI operations stack. /ph3Key Responsibilities /h3ullibDesign, maintain, and evolve the AIOps platform /b supporting:ulliTraditional machine learning models in production /lilibLLM‑based solutions such as bRAG pipelines and AI Agents /b /b /lilibSpeech Analytics /b use cases (ASR, conversation analysis, NLP) /li /ul /lilibBuild and operate ML and LLM pipelines /b with a strong focus on:ulliReliability, automation, and observability /liliModel and LLM quality, performance, and drift monitoring /liliCloud cost control and optimization /li /ul /lilibImplement LLMOps / AgentOps practices /b , including:ulliLLM evaluation and observability /liliPrompt management, traceability, and specialized logging /liliAgent integration, orchestration, and lifecycle management /li /ul /lilibEnsure continuous operation of AI products /b ,



including:ulliAlerts, dashboards, SLOs / SLIs /liliScalability strategies and basic auto‑remediation mechanisms /li /ul /lilibManage deployments in cloud environments /b (AWS / Azure) and container platforms (Docker / Kubernetes) /lilibCollaborate closely with Data Scientists and Data Engineers /b to productionize robust, scalable AI solutions /lilibContribute to internal standards, automation, and best practices /b across the AI and data ecosystem /li /ulpbRequired Skills (Must Have) /b /pulliHands‑on experience in bMLOps, AIOps, or operating ML systems in production /b /liliSolid understanding of bLLMOps and AgentOps concepts /b (RAGs, agents, evaluation, monitoring) /liliExperience working with bAWS and/or Azure /b in production environments /liliPractical knowledge of bcontainers and Kubernetes /b (Docker, basic Helm usage, etc.) /liliExperience with bCI/CD pipelines /b (GitHub Actions, GitLab CI, Azure DevOps, Jenkins, or similar) /liliFamiliarity with bobservability and monitoring concepts /b (CloudWatch, OpenTelemetry, Prometheus, etc.) /liliExperience managing infrastructure as code ( bTerraform, Bicep, CDK, or similar /b ) /lilibPython /b experience and familiarity with the ML ecosystem (e.g. scikit‑learn, PyTorch), even if not a Data Scientist /liliGood understanding of the bML / LLM lifecycle /b ,



from development to production and monitoring /lilibFluent English /b to work in an international environment /li /ulpbNice to Have (Not Required, but Valuable) /b /pulliExperience with ML/AI platforms such as bSageMaker, Azure ML, MLflow, Kubeflow /b /liliExposure to bSpeech Analytics technologies /b (ASR, diarization, conversational NLP) /liliExperience with bcloud cost optimization / FinOps /b , especially for AI workloads /liliExperience building or operating bAI agents, copilots, or conversational systems /b /liliFamiliarity with bLLM frameworks /b (LangChain, LlamaIndex, Semantic Kernel, etc.) /liliExperience with bworkflow and orchestration tools /b (Airflow, Argo, Step Functions, Durable Functions) /li /ulpbProfessional Skills Mindset /b /pulliStrong focus on breliability, automation, and scalability /b /liliAbility to collaborate effectively in bmultidisciplinary teams /b /liliClear communication and documentation‑oriented mindset /lilibPlatform mindset /b : building reusable, maintainable, and robust solutions /liliProactive, analytical, and continuous‑improvement driven /liliStrong sense of bownership and end‑to‑end responsibility /b /liliMotivation to blearn and grow across the AI operations stack /b /li /ulpbTechnology Environment /b /pullibCloud /b : AWS, Azure /lilibOrchestration Containers /b : Kubernetes, Docker /lilibCI/CD /b : GitHub Actions, GitLab CI, Azure DevOps /lilibObservability /b : Prometheus, Grafana, ELK/EFK, OpenTelemetry /lilibInfrastructure as Code /b : Terraform, Bicep, CloudFormation /lilibAI / ML Tools /b : MLflow, Azure ML, SageMaker, LangChain, LlamaIndex, Semantic Kernel /lilibPrimary Language /b : Python /li /ul /p #J-18808-Ljbffr

📌 Data Engineer (Pozuelo)
🏢 Verisure
📍 Pozuelo