Senior MLOps Engineer

emagine; Portugal; On-site; EUR 55,000 - 75,000; Full time

Job summary

emagine is seeking an experienced MLOps/LLMOps engineer in Portugal to design, build, and maintain scalable ML infrastructure on GCP. You will manage pipelines with MLflow, ZenML, Kubeflow, and Vertex AI Pipelines, and containerize environments using Docker and Kubernetes.

You will automate provisioning with Terraform and build CI/CD pipelines for ML/GenAI with GitHub Actions, LangGraph, and monitoring. Collaboration with data scientists and engineers will drive production-ready AI solutions and

Qualifications

• Strong professional experience in MLOps / LLMOps.
• Hands-on experience with GCP, 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.

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.

Skills

MLOps

LLMOps

Python

Bash

Communication

Mentoring

Problem-solving

Spanish language

English language

Tools

Vertex AI

Dataflow

BigQuery

Terraform

Docker

Kubernetes

Kubeflow

Argo Workflows

MLflow

ZenML

Vertex AI Pipelines

LangGraph

Kafka

Spark

SQL/NoSQL

Job description

• 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.

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.