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.