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.
Required Skills & Qualifications
* 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.
* 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).
* Experience working with LLMs and traditional AI / ML models, including fine-tuning, inference optimization, quantization, and serving.
* 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.
Position Location :
Remote from Portugal
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