{"id":2160379,"url":"https://alion.io/job/talentcross-mlops-engineer","title":"MLOps Engineer","company":{"id":3853091,"name":"Talentcross","domain":"talentcross.it","url":"https://alion.io/company/talentcross","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Manatal","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EKS","optional":false},{"name":"Ansible","optional":false},{"name":"Apache 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Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"RAG","optional":false},{"name":"Redpanda","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"TimescaleDB","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-10-09T08:15:32Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-11T00:28:29Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Si disfrutas optimizando entornos de producción, asegurando escalabilidad, confiabilidad y trabajando con tecnologías de vanguardia en observabilidad, Agentes IA y Machine Learning, ¡te queremos en nuestro equipo!\n¿Qué harás como MLOps?\n Diseñar, desplegar y gestionar infraestructuras cloud escalables en AWS, GCP o Azure, optimizadas para entornos de datos y ML.\n Automatizar procesos mediante Terraform, Ansible, Helm y Kubernetes.\n Implementar pipelines de CI/CD para la entrega continua de modelos, agentes y soluciones de observabilidad.\n Administrar contenedores y orquestación con Docker y Kubernetes (EKS), integrando servicios de datos, IA y backend.\n Desarrollar y mantener pipelines de entrenamiento, validación e inferencia reproducibles (Airflow, MLflow, DVC, Spark).\n Gestionar y optimizar sistemas de monitoreo y logging con Prometheus, Grafana, ELK/EFK o OpenTelemetry.\n Garantizar la seguridad, resiliencia y disponibilidad de las plataformas en la nube.\n Integrar el ciclo completo Data ML Deployment Monitoring, colaborando con equipos de Data, AI y DevOps.\n Colaborar con equipos de Machine Learning para empaquetar y desplegar modelos y agentes IA en producción.\n (Plus avanzado) Participar en el diseño de pipelines híbridos que integren ML tradicional, IA generativa y agentes basados en LLM (LangGraph, LangChain, CrewAI).\nLo que necesitamos de ti\n Formación en Ingeniería Informática, Software, Telecomunicaciones o áreas afines.\n +3 años de experiencia en automatización, despliegue o administración de infraestructura en la nube.\n Experiencia sólida en infraestructura como código (Terraform, Helm, Ansible) y automatización de despliegues.\n Conocimiento profundo de Linux, redes, y administración de sistemas distribuidos.\n Experiencia práctica con CI/CD (GitLab CI, Jenkins, ArgoCD, FluxCD).\n Conocimientos en contenedores y orquestación (Docker, Kubernetes).\n Experiencia en MLflow, DVC o Vertex AI para gestión de modelos y datasets.\n Conocimiento en monitorización, logging y observabilidad (Prometheus, Grafana, ELK, OpenTelemetry).\n Familiaridad con bases de datos SQL y NoSQL (PostgreSQL, TimescaleDB, MongoDB).\n Habilidad para resolver incidencias en sistemas productivos de alto tráfico, optimizando disponibilidad y costos.\n Habilidades de comunicación y colaboración para trabajar con equipos multidisciplinarios (Data, ML, Backend, Infra).\nSi además tienes ¡Suma puntos extra!\n Experiencia con Kafka, Redpanda o Benthos en sistemas de mensajería o streaming.\n Conocimientos en serverless y arquitecturas event-driven (AWS Lambda, SNS/SQS).\n Experiencia con observabilidad avanzada y métricas de rendimiento de modelos y sistemas.\n Conocimientos en SRE (Site Reliability Engineering).\n Experiencia con MLOps de última generación (Kubeflow, MLflow).\n Familiaridad con prácticas para despliegues automatizados.\n Conocimientos en infraestructura para IA generativa (GPU, containers optimizados, RAG serving).\n Nivel de inglés técnico avanzado, tanto oral como escrito, para comunicación con equipos y partners 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