Senior MLOps Engineer

Intellias; Gijón; A distancia; EUR 50.000 - 80.000; Jornada completa

Descripción de la vacante

A technology services company is seeking a Senior MLOps Engineer to work remotely from Spain. The ideal candidate will possess expertise in Google Cloud Platform and significant experience in building and deploying AI and ML pipelines. Responsibilities include designing GCP architectures and managing data workflows, while ensuring compliance and governance best practices. This role offers a dynamic work environment with ample opportunities for professional development.

Formación

• Experience with MLOps / DevOps / Data Engineering for at least 3 years.
• Hands-on experience in building AI/ML pipelines and workflows.
• Proficiency in security best practices for ML models.

Responsabilidades

• Build, deploy, and automate AI and ML pipelines on Google Cloud Platform.
• Deploy and optimize Large Language Models (LLMs) and other AI/ML models.
• Design data ingestion and processing workflows using Apache Airflow.

Conocimientos

MLOps

Google Cloud Platform

Apache Airflow

Terraform

Python

AI / ML pipelines

Kubernetes

Educación

4-year degree in a relevant field

Herramientas

Google Kubernetes Engine

Apache Spark

BigQuery

Hugging Face Transformers

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
• Proven ability to build, deploy, and maintain AI models in 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