Alicante
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. /p pThis position is bstrongly focused on platform, infrastructure, automation, observability, and operations /b rather than on building ML models or AI products themselves. /p pYou 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. /p pWe 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. /p h3Key Responsibilities /h3 ul liDesign, maintain, and evolve the AIOps platform supporting: ul liTraditional machine learning models in production /li liLLM‑based solutions such as RAG pipelines and AI Agents /li liSpeech Analytics use cases (ASR, conversation analysis, NLP) /li /ul /li liBuild and operate ML and LLM pipelines with a strong focus on: ul liReliability, automation, and observability /li liModel and LLM quality, performance, and drift monitoring /li liCloud cost control and optimization /li /ul /li liImplement LLMOps / AgentOps practices, including: ul liLLM evaluation and observability /li liPrompt management, traceability, and specialized logging /li liAgent integration, orchestration, and lifecycle management /li /ul /li liEnsure continuous operation of AI products, including: ul liAlerts, dashboards, SLOs / SLIs /li liScalability strategies and basic auto‑remediation mechanisms /li /ul /li liManage deployments in cloud environments (AWS / Azure) and container platforms (Docker / Kubernetes) /li liCollaborate closely with Data Scientists and Data Engineers to productionize robust, scalable AI solutions /li liContribute to internal standards, automation, and best practices across the AI and data ecosystem /li /ul h3Required Skills (Must Have) /h3 ul liHands‑on experience in MLOps, AIOps, or operating ML systems in production /li liSolid understanding of LLMOps and AgentOps concepts (RAGs, agents, evaluation, monitoring) /li liExperience working with AWS and/or Azure in production environments /li liPractical knowledge of containers and Kubernetes (Docker, basic Helm usage, etc.) /li liExperience with CI/CD pipelines (GitHub Actions, GitLab CI, Azure DevOps, Jenkins, or similar) /li liFamiliarity with observability and monitoring concepts (CloudWatch, OpenTelemetry, Prometheus, etc.) /li liExperience managing infrastructure as code (Terraform, Bicep, CDK, or similar) /li liPython experience and familiarity with the ML ecosystem (e.g. scikit‑learn, PyTorch), even if not a Data Scientist /li liGood understanding of the ML / LLM lifecycle, from development to production and monitoring /li liFluent English to work in an international environment /li /ul h3Nice To Have (Not Required, But Valuable) /h3 ul liExperience with ML/AI platforms such as SageMaker, Azure ML, MLflow, Kubeflow /li liExposure to Speech Analytics technologies (ASR, diarization, conversational NLP) /li liExperience with cloud cost optimization / FinOps, especially for AI workloads /li liExperience building or operating AI agents, copilots, or conversational systems /li liFamiliarity with LLM frameworks (LangChain, LlamaIndex, Semantic Kernel, etc.) /li liExperience with workflow and orchestration tools (Airflow, Argo, Step Functions, Durable Functions) /li /ul h3Professional Skills Mindset /h3 ul liStrong focus on reliability, automation, and scalability /li liAbility to collaborate effectively in multidisciplinary teams /li liClear communication and documentation‑oriented mindset /li liPlatform mindset: building reusable, maintainable, and robust solutions /li liProactive, analytical, and continuous‑improvement driven /li liStrong sense of ownership and end‑to‑end responsibility /li liMotivation to learn and grow across the AI operations stack /li /ul h3Technology Environment /h3 ul liCloud: AWS, Azure /li liOrchestration Containers: Kubernetes, Docker /li liCI/CD: GitHub Actions, GitLab CI, Azure DevOps /li liObservability: Prometheus, Grafana, ELK/EFK, OpenTelemetry /li liInfrastructure as Code: Terraform, Bicep, CloudFormation /li liAI / ML Tools: MLflow, Azure ML, SageMaker, LangChain, LlamaIndex, Semantic Kernel /li liPrimary Language: Python /li /ul /p #J-18808-Ljbffr
Suministros MarcalActive 5 days ago
