Data Engineer

We are looking for aMLOps / AIOps / LLMOps / AgentOps Engineerto join a multidisciplinary Data & AI team.The main mission of this role is todesign, operate, and continuously evolve our AIOps platform, ensuring that our AI products run in areliable, scalable, and cost?efficientway.¿Es este su próximo empleo?Descúbralo leyendo la descripción completa a continuación y no dude en enviar su candidatura.This position isstrongly focused on platform, infrastructure, automation, observability, and operationsrather than on building ML models or AI products themselves.You will work with modern cloud technologies (mainlyAWS, with someAzureexposure) and collaborate closely withData Scientists, Data Engineers, and Product teamsto bring AI solutions into production and keep them running smoothly.We are open to candidates withstrong expertise in at least one core area(e.g. cloud, DevOps, platform engineering, or ML operations) andsolid foundational knowledge in the others, with motivation to grow across the full AI operations stack.Key ResponsibilitiesDesign, maintain, and evolve the AIOps platformsupporting:Traditional machine learning models in productionLLM?based solutions such asRAG pipelines and AI AgentsSpeech Analyticsuse cases (ASR, conversation analysis, NLP)Build and operate ML and LLM pipelineswith a strong focus on:Reliability, automation, and observabilityModel and LLM quality, performance, and drift monitoringCloud cost control and optimizationImplement LLMOps / AgentOps practices, including:LLM evaluation and observabilityPrompt management, traceability, and specialized loggingAgent integration, orchestration, and lifecycle managementEnsure continuous operation of AI products, including:Alerts, dashboards, SLOs / SLIsScalability strategies and basic auto?remediation mechanismsManage deployments in cloud environments(AWS / Azure) and container platforms (Docker / Kubernetes)Collaborate closely with Data Scientists and Data Engineersto productionize robust, scalable AI solutionsContribute to internal standards, automation, and best practicesacross the AI and data ecosystemRequired Skills (Must Have)Hands?on experience inMLOps, AIOps, or operating ML systems in productionSolid understanding ofLLMOps and AgentOps concepts(RAGs, agents, evaluation, monitoring)Experience working withAWS and/or Azurein production environmentsPractical knowledge ofcontainers and Kubernetes(Docker, basic Helm usage, etc.)Experience withCI/CD pipelines(GitHub Actions, GitLab CI, Azure DevOps, Jenkins, or similar)Familiarity withobservability and monitoring concepts(CloudWatch, OpenTelemetry, Prometheus, etc.)Experience managing infrastructure as code (Terraform, Bicep, CDK, or similar)Pythonexperience and familiarity with the ML ecosystem (e.g. scikit?learn, PyTorch), even if not a Data ScientistGood understanding of theML / LLM lifecycle, from development to production and monitoringFluent Englishto work in an international environmentNice to Have (Not Required, but Valuable)Experience with ML/AI platforms such asSageMaker, Azure ML, MLflow, KubeflowExposure toSpeech Analytics technologies(ASR, diarization, conversational NLP)Experience withcloud cost optimization / FinOps, especially for AI workloadsExperience building or operatingAI agents, copilots, or conversational systemsFamiliarity xqbhyrx withLLM frameworks(LangChain, LlamaIndex, Semantic Kernel, etc.)Experience withworkflow and orchestration tools(Airflow, Argo, Step Functions, Durable Functions)Professional Skills & MindsetStrong focus onreliability, automation, and scalabilityAbility to collaborate effectively inmultidisciplinary teamsClear communication and documentation?oriented mindsetPlatform mindset: building reusable, maintainable, and robust solutionsProactive, analytical, and continuous?improvement drivenStrong sense ofownership and end?to?end responsibilityMotivation tolearn and grow across the AI operations stackTechnology EnvironmentCloud: AWS, AzureOrchestration & Containers: Kubernetes, DockerCI/CD: GitHub Actions, GitLab CI, Azure DevOpsObservability: Prometheus, Grafana, ELK/EFK, OpenTelemetryInfrastructure as Code: Terraform, Bicep, CloudFormationAI / ML Tools: MLflow, Azure ML, SageMaker, LangChain, LlamaIndex, Semantic KernelPrimary Language: Python#J-*****-Ljbffr

Required Skills (Must Have) Hands?on experience inMLOps, AIOps, or operating ML systems in production Solid understanding ofLLMOps and AgentOps concepts(RAGs, agents, evaluation, monitoring) Experience working withAWS and/or Azurein production environments Practical knowledge ofcontainers and Kubernetes(Docker, basic Helm usage, etc.) Experience withCI/CD pipelines(GitHub Actions, GitLab CI, Azure DevOps, Jenkins, or similar) Familiarity withobservability and monitoring concepts(CloudWatch, OpenTelemetry, Prometheus, etc.) Experience managing infrastructure as code (Terraform, Bicep, CDK, or similar) Pythonexperience and familiarity with the ML ecosystem (e.g. scikit?learn, PyTorch), even if not a Data Scientist Good understanding of theML / LLM lifecycle, from development to production and monitoring Fluent Englishto work in an international environment Nice to Have (Not Required, but Valuable) Experience with ML/AI platforms such asSageMaker, Azure ML, MLflow, Kubeflow Exposure toSpeech Analytics technologies(ASR, diarization, conversational NLP) Experience withcloud cost optimization / FinOps, especially for AI workloads Experience building or operatingAI agents, copilots, or conversational systems Familiarity xqbhyrx withLLM frameworks(LangChain, LlamaIndex, Semantic Kernel, etc.) Experience withworkflow and orchestration tools(Airflow, Argo, Step Functions, Durable Functions) Professional Skills & Mindset Strong focus onreliability, automation, and scalability Ability to collaborate effectively inmultidisciplinary teams Clear communication and documentation?oriented mindset Platform mindset: building reusable, maintainable, and robust solutions Proactive, analytical, and continuous?improvement driven Strong sense ofownership and end?to?end responsibility Motivation tolearn and grow across the AI operations stack Technology Environment Cloud: AWS, Azure Orchestration & Containers: Kubernetes, Docker CI/CD: GitHub Actions, GitLab CI, Azure DevOps Observability: Prometheus, Grafana, ELK/EFK, OpenTelemetry Infrastructure as Code: Terraform, Bicep, CloudFormation AI / ML Tools: MLflow, Azure ML, SageMaker, LangChain, LlamaIndex, Semantic Kernel Primary Language: Python #J-*****-Ljbffr