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.