Senior MLOps Engineer
Summary
A Senior MLOps Engineer who designs, builds, and maintains MLOps/LLMOps infrastructure on GCP: ML pipelines (MLflow, ZenML, Kubeflow, Vertex AI), Docker/Kubernetes, Terraform, CI/CD via GitHub Actions, LangGraph-based LLM/agent workflows, LLM/RAG evaluation and monitoring, plus data pipelines with Dataflow/Apache Beam.
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.