Senior MLOps Engineer

Responsibilities - Design, build, and maintain scalable MLOps and LLMOps infrastructure on GCP. - Develop and manage ML pipelines using MLflow, ZenML, Kubeflow, and Vertex AI Pipelines. - Manage containerized environments using Docker and Kubernetes. - Automate infrastructure provisioning with Terraform. - Build and maintain CI/CD pipelines for ML and GenAI applications using GitHub Actions. - Design and orchestrate LLM and agentic workflows using LangGraph. - Implement evaluation frameworks for LLMs, RAG systems, and AI agents. - Monitor ML and GenAI applications, including model performance, drift, tracing, and observability. - Develop scalable data processing pipelines using Dataflow and Apache Beam. - Collaborate with Data Scientists and Engineering teams to deliver production-ready AI solutions. - Provide technical guidance and promote MLOps and LLMOps best practices across the team. Requirements - Strong professional experience in MLOps / LLMOps. - Hands-on experience with GCP, particularly Vertex AI, Dataflow, and BigQuery. - Strong knowledge of Terraform, Docker, and Kubernetes. - Experience with Kubeflow, Argo Workflows, MLflow, ZenML, and/or Vertex AI Pipelines. - Strong Python and Bash skills. - Experience building CI/CD pipelines for ML and/or GenAI applications. - Practical experience with Generative AI and LLM applications. - Experience with LangGraph or similar AI orchestration frameworks. - Knowledge of LLM evaluation, RAG evaluation, model monitoring, and drift detection. - Familiarity with Kafka, Spark, SQL/NoSQL databases, and Data Lakes. - Strong communication, problem-solving, and mentoring skills. - Spanish: B2 minimum, C1 preferred. - English: B2 minimum.