[WW-234] - Senior MLOps Engineer

Intellias; Lérida; A distancia; EUR 50.000 - 70.000; Jornada completa

Descripción de la vacante

Una empresa tecnológica busca un Ingeniero Senior de MLOps para diseñar y optimizar tuberías de IA y ML en Google Cloud Platform. Se requiere experiencia en GCP, implementación de LLMs y conocimiento en herramientas como Kubeflow y Apache Airflow. Esta posición es remota desde España y ofrece contratación bajo legislación española.

Formación

• Título de 4 años preferido; se considerará experiencia relevante.
• Más de 3 años de experiencia en MLOps/DevOps/Ingeniería de Datos.
• Experiencia práctica construyendo tuberías de IA/ML y flujos de trabajo de ingeniería de datos.
• Competencia en Python y familiaridad con Bash, Scala o Terraform.

Responsabilidades

• Construir, implementar y automatizar tuberías de IA y ML en Google Cloud Platform.
• Desplegar, optimizar y escalar Modelos de Lenguaje Grande (LLMs).
• Implementar monitoreo y detección de desviaciones para modelos de IA/ML.
• Diseñar arquitecturas nativas de la nube con GCP como plataforma principal.

Descripción del empleo

Job Description: Senior MLOps Engineer

Location: Remote from Spain (Spanish employment contract)

We are seeking an experienced MLOps Engineer with expertise in Google Cloud Platform (GCP) to design, build, and optimize end-to-end AI, ML, and data engineering pipelines. This role involves deploying machine learning models, LLMs, and traditional AI models, as well as managing data processing workflows in a GCP-first environment.

The ideal candidate will have experience working with Google Kubernetes Engine (GKE), Apache Spark, Dataproc, Terraform, Vertex AI, and Airflow (Cloud Composer) to ensure scalable and efficient AI/ML operations. While Amazon Web Services (AWS) experience is a plus, it is not required.

Requirements:





- 4-year degree preferred relevant experience will be considered
- 3+ years of MLOps/DevOps/Data Engineering experience, with expertise in Google Cloud Platform (Vertex AI, Dataproc, BigQuery, Cloud Functions, Cloud Composer, GKE).
- Hands-on experience building AI/ML pipelines and data engineering workflows using Apache Airflow (Cloud Composer), Spark, Databricks, and distributed data processing frameworks.
- Experience working with LLMs and traditional AI/ML models, including fine-tuning, inference optimization, quantization, and serving.
- Proficiency in CI/CD for ML, version control (Git), and workflow orchestration (Airflow, Kubeflow, MLflow).
- Strong experience with Terraform for infrastructure automation.
- Strong knowledge of Apigee for deploying, managing, and securing machine learning APIs at scale.
- Production-ready AI/ML solutions: Proven ability to build, deploy, and maintain AI modelsin real-world production environments.
- Programming Skills: Proficiency in Python and familiarity with Bash, Scala, or Terraform scripting.




- Experience with security best practices for ML models, including IAM, data encryption, and model governance.

Bonus Qualifications/Experience:

- Experience with multi-cloud AI/ML solutions.
- Familiarity with AWS AI/ML services (SageMaker, EMR, Lambda, EKS, DynamoDB).
- Knowledge of Feature Stores (Feast, Vertex AI Feature Store, AWS Feature Store).
- Understanding of AIOps and ML observability tools.
- Experience with real-time AI inference pipelines and low-latency model serving.
- Gitlab CI/CD with focus on CI/CD for GCP deployments
- Experience working with PHI/PII in HIPAA and/or GDPR compliant environments

Responsibilities:

- Build, deploy, and automate AI and ML pipelines on Google Cloud Platform (GCP) using tools such as Vertex AI, BigQuery, Dataproc, Cloud Functions, and GKE.
- Deploy, optimize, and scale Large Language Models (LLMs)



and other AI/ML models using platforms like Hugging Face Transformers, OpenAI API, Google Gemini, Meta Llama, TensorFlow, and PyTorch.
- Design and manage data ingestion, transformation, and processing workflows using Apache Airflow (Cloud Composer), Spark, Databricks, and ETL pipelines.
- Deploy AI/ML models and data services using Docker, Kubernetes (GKE), Helm, and serverless architectures including Cloud Run.
- Automate and manage ML/AI deployments using Infrastructure as Code tools such as Terraform and CI/CD pipelines with GitHub Actions or GitLab.
- Develop scalable, fault-tolerant ML pipelines to train, deploy, and monitor models in production environments.
- Deploy AI models using TensorFlow Serving, TorchServe, FastAPI, Flask, and GCP-native serverless technologies like Cloud Run.
- Implement monitoring, drift detection,



and performance tracking for AI/ML models using MLflow, Prometheus, Grafana, and Vertex AI Model Monitoring.
- Ensure security, governance, access control, and compliance best practices across AI and ML workflows.
- Design cloud-native architectures with GCP as the core platform, utilizing its AI/ML and data engineering tools.

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